CartesiaTTSService: reset context id when flushing audio

2025-02-11 23:36:08 -08:00
113 changed files with 864 additions and 18905 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -32,21 +32,6 @@ fly.toml

 # Example files
 pipecat/examples/twilio-chatbot/templates/streams.xml
-pipecat/examples/bot-ready-signalling/client/react-native/node_modules/
-pipecat/examples/bot-ready-signalling/client/react-native/.expo/
-pipecat/examples/bot-ready-signalling/client/react-native/dist/
-pipecat/examples/bot-ready-signalling/client/react-native/npm-debug.*
-pipecat/examples/bot-ready-signalling/client/react-native/*.jks
-pipecat/examples/bot-ready-signalling/client/react-native/*.p8
-pipecat/examples/bot-ready-signalling/client/react-native/*.p12
-pipecat/examples/bot-ready-signalling/client/react-native/*.key
-pipecat/examples/bot-ready-signalling/client/react-native/*.mobileprovision
-pipecat/examples/bot-ready-signalling/client/react-native/*.orig.*
-pipecat/examples/bot-ready-signalling/client/react-native/web-build/
-
-# macOS
-.DS_Store
-

 # Documentation
 docs/api/_build/
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,113 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

-## Unreleased
+## [Unreleased]

 ### Added

- Added retry logic to `get_chat_completions()` fo all OpenAI-based LLM
-  services.
-
- Added new log observers `LLMLogObserver` and `TranscriptionLogObserver` that
-  can be useful for debugging your pipelines.
-
- Added `room_url` property to `DailyTransport`.
-
- Added `addons` argument to `DeepgramSTTService`.
-
- Added `exponential_backoff_time()` to `utils.network` module.
-
-### Changed
-
- `RimeHttpTTSService` needs an `aiohttp.ClientSession` to be passed to the
-  constructor as all the other HTTP-based services.
-
- `RimeHttpTTSService` doesn't use a default voice anymore.
-
- `DeepgramSTTService` now uses the new `nova-3` model by default. If you want
-  to use the previous model you can pass `LiveOptions(model="nova-2-general")`.
-  (see https://deepgram.com/learn/introducing-nova-3-speech-to-text-api)
-
-```python
-stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
-```
-
-### Removed
-
- Remove `TransportParams.audio_out_is_live` since it was not being used at all.
-
-### Fixed
-
- Fixed `match_endofsentence` support for ellipses.
-
- Fixed an issue that would cause undesired interruptions via
-  `EmulateUserStartedSpeakingFrame` when only interim transcriptions (i.e. no
-  final transcriptions) where received.
-
- Fixed an issue where `EndTaskFrame` was not triggering
-  `on_client_disconnected` or closing the WebSocket in FastAPI.
-
- Fixed an issue in `DeepgramSTTService` where the `sample_rate` passed to the
-  `LiveOptions` was not being used, causing the service to use the default
-  sample rate of pipeline.
-
- Fixed a context aggregator issue that would not append the LLM text response
-  to the context if a function call happened in the same LLM turn.
-
- Fixed an issue that was causing HTTP TTS services to push `TTSStoppedFrame`
-  more than once.
-
- Fixed a `FishAudioTTSService` issue where `TTSStoppedFrame` was not being
-  pushed.
-
- Fixed an issue that `start_callback` was not invoked for some LLM services.
-
- Fixed an issue that would cause `DeepgramSTTService` to stop working after an
-  error occurred (e.g. sudden network loss). If the network recovered we would
-  not reconnect.
-
- Fixed a `STTMuteFilter` issue that would not mute user audio frames causing
-  transcriptions to be generated by the STT service.
-
-### Other
-
- Added Gemini support to `examples/phone-chatbot`.
-
-## [0.0.57] - 2025-02-14
-
-### Added
-
- Added new `AudioContextWordTTSService`. This is a TTS base class for TTS
-  services that handling multiple separate audio requests.
-
- Added new frames `EmulateUserStartedSpeakingFrame` and
-  `EmulateUserStoppedSpeakingFrame` which can be used to emulated VAD behavior
-  without VAD being present or not being triggered.
-
- Added a new `audio_in_stream_on_start` field to `TransportParams`.
-
- Added a new method `start_audio_in_streaming` in the `BaseInputTransport`.
-
-  - This method should be used to start receiving the input audio in case the
-    field `audio_in_stream_on_start` is set to `false`.
-
- Added support for the `RTVIProcessor` to handle buffered audio in `base64`
-  format, converting it into InputAudioRawFrame for transport.
-
- Added support for the `RTVIProcessor` to trigger `start_audio_in_streaming`
-  only after the `client-ready` message.
-
- Added new `MUTE_UNTIL_FIRST_BOT_COMPLETE` strategy to `STTMuteStrategy`. This
-  strategy starts muted and remains muted until the first bot speech completes,
-  ensuring the bot's first response cannot be interrupted. This complements the
-  existing `FIRST_SPEECH` strategy which only mutes during the first detected
-  bot speech.
-
- Added support for Google Cloud Speech-to-Text V2 through `GoogleSTTService`.
-
- Added `RimeTTSService`, a new `WordTTSService`. Updated the foundational
-  example `07q-interruptible-rime.py` to use `RimeTTSService`.
-
 - Added support for Groq's Whisper API through the new `GroqSTTService` and
  OpenAI's Whisper API through the new `OpenAISTTService`. Introduced a new
  base class `BaseWhisperSTTService` to handle common Whisper API
@@ -121,33 +18,10 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
  OpenAI-compatible interface. Also, added foundational example
  `14n-function-calling-perplexity.py`.

- Added `DailyTransport.update_remote_participants()`. This allows you to update
-  remote participant's settings, like their permissions or which of their
-  devices are enabled. Requires that the local participant have participant
-  admin permission.
+- Added `DailyTransport.update_remote_participants()`. This allows you to update remote participant's settings, like their permissions or which of their devices are enabled. Requires that the local participant have participant admin permission.

 ### Changed

- We don't consider a colon `:` and end of sentence any more.
-
- Updated `DailyTransport` to respect the `audio_in_stream_on_start` field,
-  ensuring it only starts receiving the audio input if it is enabled.
-
- Updated `FastAPIWebsocketOutputTransport` to send `TransportMessageFrame` and
-  `TransportMessageUrgentFrame` to the serializer.
-
- Updated `WebsocketServerOutputTransport` to send `TransportMessageFrame` and
-  `TransportMessageUrgentFrame` to the serializer.
-
- Enhanced `STTMuteConfig` to validate strategy combinations, preventing
-  `MUTE_UNTIL_FIRST_BOT_COMPLETE` and `FIRST_SPEECH` from being used together
-  as they handle first bot speech differently.
-
- Updated foundational example `07n-interruptible-google.py` to use all Google
-  services.
-
- `RimeHttpTTSService` now uses the `mistv2` model by default.
-
 - Improved error handling in `AzureTTSService` to properly detect and log
  synthesis cancellation errors.

@@ -161,8 +35,7 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
  model.

 - `RTVIObserver` doesn't handle `LLMSearchResponseFrame` frames anymore. For
-  now, to handle those frames you need to create a `GoogleRTVIObserver`
-  instead.
+  now, to handle those frames you need to create a `GoogleRTVIObserver` instead.

 ### Deprecated

@@ -179,28 +52,8 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))

 ### Fixed

- Fixed a `FalImageGenService` issue that was causing the event loop to be
-  blocked while loading the downloadded image.
-
- Fixed a `CartesiaTTSService` service issue that would cause audio overlapping
-  in some cases.
-
- Fixed a websocket-based service issue (e.g. `CartesiaTTSService`) that was
-  preventing a reconnection after the server disconnected cleanly, which was
-  causing an inifite loop instead.
-
- Fixed a `BaseOutputTransport` issue that was causing upstream frames to no be
-  pushed upstream.
-
- Fixed multiple issue where user transcriptions where not being handled
-  properly. It was possible for short utterances to not trigger VAD which would
-  cause user transcriptions to be ignored. It was also possible for one or more
-  transcriptions to be generated after VAD in which case they would also be
-  ignored.
-
- Fixed an issue that was causing `BotStoppedSpeakingFrame` to be generated too
-  late. This could then cause issues unblocking `STTMuteFilter` later than
-  desired.
+- Fixed a `CartesiaTTSService` issue that could cause audio overlapping in some
+  cases.

 - Fixed an issue that was causing `AudioBufferProcessor` to not record
  synchronized audio.
@@ -208,20 +61,8 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 - Fixed an `RTVI` issue that was causing `bot-tts-text` messages to be sent
  before being processed by the output transport.

- Fixed an issue[#1192] in 11labs where we are trying to reconnect/disconnect
-  the websocket connection even when the connection is already closed.
-
- Fixed an issue where `has_regular_messages` condition was always true in
-  `GoogleLLMContext` due to `Part` having `function_call` & `function_response`
-  with `None` values.
-
-### Other
-
- Added new `instant-voice` example. This example showcases how to enable
-  instant voice communication as soon as a user connects.
-
- Added new `local-input-select-stt` example. This examples allows you to play
-  with local audio inputs by slecting them through a nice text interface.
+- Fixed an issue[#1192] in 11labs where we are trying to reconnect/disconnect the
+  websocket connection even when the connection is already closed.

 ## [0.0.56] - 2025-02-06

@@ -242,8 +83,7 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
  logging setup.

 - Fixed a `SentryMetrics` issue that was preventing any metrics to be sent to
-  Sentry and also was preventing from metrics frames to be pushed to the
-  pipeline.
+  Sentry and also was preventing from metrics frames to be pushed to the pipeline.

 - Fixed an issue in `BaseOutputTransport` where incoming audio would not be
  resampled to the desired output sample rate.
@@ -402,8 +242,7 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))

 - Added `enable_recording` and `geo` parameters to `DailyRoomProperties`.

- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings
-  to a custom AWS bucket.
+- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings to a custom AWS bucket.

 ### Changed

--- a/README.md
+++ b/README.md
@@ -57,7 +57,7 @@ pip install "pipecat-ai[option,...]"

 | Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Install Command Example                 |
 | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[deepgram]"`    |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                                                            | `pip install "pipecat-ai[deepgram]"`    |
 | LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
 | Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                                                                                                                                                                          | `pip install "pipecat-ai[cartesia]"`    |
 | Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[google]"`      |
--- a/codecov.yml
+++ b/codecov.yml
@@ -1,11 +0,0 @@
-coverage:
-  range: 50..90 # coverage lower than 50 is red, higher than 90 green, between color code
-
-  status:
-    project:
-      default:
-        target: auto # auto % coverage target
-        threshold: 5%  # allow for 5% reduction of coverage without failing
-
-    # do not run coverage on patch nor changes
-    patch: false
--- a/examples/bot-ready-signalling/client/react-native/.nvmrc
+++ b/examples/bot-ready-signalling/client/react-native/.nvmrc
@@ -1 +0,0 @@
-22.14
--- a/examples/bot-ready-signalling/client/react-native/README.md
+++ b/examples/bot-ready-signalling/client/react-native/README.md
@@ -1,60 +0,0 @@
-# React Native Implementation
-
-Basic implementation using the [Pipecat React Native SDK](https://docs.pipecat.ai/client/react-native/introduction).
-
-## Usage
-
-### Expo requirements
-
-This project cannot be used with an [Expo Go](https://docs.expo.dev/workflow/expo-go/) app because [it requires custom native code](https://docs.expo.io/workflow/customizing/).
-
-When a project requires custom native code or a config plugin, we need to transition from using [Expo Go](https://docs.expo.dev/workflow/expo-go/) 
-to a [development build](https://docs.expo.dev/development/introduction/).
-
-More details about the custom native code used by this demo can be found in [rn-daily-js-expo-config-plugin](https://github.com/daily-co/rn-daily-js-expo-config-plugin).
-
-### Building remotely
-
-If you do not have experience with Xcode and Android Studio builds or do not have them installed locally on your computer, you will need to follow [this guide from Expo to use EAS Build](https://docs.expo.dev/development/create-development-builds/#create-and-install-eas-build).
-
-### Building locally
-
-You will need to have installed locally on your computer:
- [Xcode](https://developer.apple.com/xcode/) to build for iOS;
- [Android Studio](https://developer.android.com/studio) to build for Android;
-
-#### Install the demo dependencies
-
-```bash
-# Use the version of node specified in .nvmrc
-nvm i
-
-# Install dependencies
-npm i
-
-# Before a native app can be compiled, the native source code must be generated.
-npx expo prebuild
-
-# Configure the environment variable to connect to the local server
-cp env.example .env
-# edit .env and add your local ip address, for example: http://192.168.1.16:7860
-```
-
-#### Running on Android
-
-After plugging in an Android device [configured for debugging](https://developer.android.com/studio/debug/dev-options), run the following command:
-
-```
-npm run android
-```
-
-#### Running on iOS
-
-Run the following command:
-
-```
-npm run ios
-```
-
-#### Connect to the server
-Use the http://localhost:5173 in your app.
--- a/examples/bot-ready-signalling/client/react-native/app.json
+++ b/examples/bot-ready-signalling/client/react-native/app.json
@@ -1,75 +0,0 @@
-{
-  "expo": {
-    "name": "bot-ready-rn",
-    "slug": "bot-ready-rn",
-    "version": "1.0.0",
-    "orientation": "portrait",
-    "icon": "./assets/icon.png",
-    "userInterfaceStyle": "light",
-    "splash": {
-      "image": "./assets/splash.png",
-      "resizeMode": "contain",
-      "backgroundColor": "#ffffff"
-    },
-    "updates": {
-      "fallbackToCacheTimeout": 0
-    },
-    "assetBundlePatterns": [
-      "**/*"
-    ],
-    "ios": {
-      "supportsTablet": true,
-      "bitcode": false,
-      "bundleIdentifier": "co.daily.expo.BotReady",
-      "infoPlist": {
-        "UIBackgroundModes": [
-          "voip"
-        ]
-      },
-      "appleTeamId": "EEBGKV9N3N"
-    },
-    "android": {
-      "adaptiveIcon": {
-        "foregroundImage": "./assets/adaptive-icon.png",
-        "backgroundColor": "#FFFFFF"
-      },
-      "package": "co.daily.expo.BotReady",
-      "permissions": [
-        "android.permission.ACCESS_NETWORK_STATE",
-        "android.permission.BLUETOOTH",
-        "android.permission.CAMERA",
-        "android.permission.INTERNET",
-        "android.permission.MODIFY_AUDIO_SETTINGS",
-        "android.permission.RECORD_AUDIO",
-        "android.permission.SYSTEM_ALERT_WINDOW",
-        "android.permission.WAKE_LOCK",
-        "android.permission.FOREGROUND_SERVICE",
-        "android.permission.FOREGROUND_SERVICE_CAMERA",
-        "android.permission.FOREGROUND_SERVICE_MICROPHONE",
-        "android.permission.FOREGROUND_SERVICE_MEDIA_PROJECTION",
-        "android.permission.POST_NOTIFICATIONS"
-      ]
-    },
-    "web": {
-      "favicon": "./assets/favicon.png"
-    },
-    "plugins": [
-      "@config-plugins/react-native-webrtc",
-      "@daily-co/config-plugin-rn-daily-js",
-      [
-        "expo-build-properties",
-        {
-          "android": {
-            "minSdkVersion": 24,
-            "compileSdkVersion": 35,
-            "targetSdkVersion": 34,
-            "buildToolsVersion": "35.0.0"
-          },
-          "ios": {
-            "deploymentTarget": "15.1"
-          }
-        }
-      ]
-    ]
-  }
-}
--- a/examples/bot-ready-signalling/client/react-native/assets/adaptive-icon.png
+++ b/examples/bot-ready-signalling/client/react-native/assets/adaptive-icon.png
--- a/examples/bot-ready-signalling/client/react-native/assets/favicon.png
+++ b/examples/bot-ready-signalling/client/react-native/assets/favicon.png
--- a/examples/bot-ready-signalling/client/react-native/assets/icon.png
+++ b/examples/bot-ready-signalling/client/react-native/assets/icon.png
--- a/examples/bot-ready-signalling/client/react-native/assets/splash.png
+++ b/examples/bot-ready-signalling/client/react-native/assets/splash.png
--- a/examples/bot-ready-signalling/client/react-native/babel.config.js
+++ b/examples/bot-ready-signalling/client/react-native/babel.config.js
@@ -1,7 +0,0 @@
-module.exports = function(api) {
-  api.cache(true);
-  return {
-    presets: ['babel-preset-expo'],
-    plugins: [["module:react-native-dotenv"]],
-  };
-};
--- a/examples/bot-ready-signalling/client/react-native/env.example
+++ b/examples/bot-ready-signalling/client/react-native/env.example
@@ -1 +0,0 @@
-API_BASE_URL=http://YOUR_LOCAL_IP:7860
--- a/examples/bot-ready-signalling/client/react-native/index.js
+++ b/examples/bot-ready-signalling/client/react-native/index.js
@@ -1,7 +0,0 @@
-import { registerRootComponent } from "expo";
-
-import App from "./src/App";
-
-// registerRootComponent calls AppRegistry.registerComponent('main', () => App);
-// It also ensures that the environment is set up appropriately
-registerRootComponent(App);
--- a/examples/bot-ready-signalling/client/react-native/metro.config.js
+++ b/examples/bot-ready-signalling/client/react-native/metro.config.js
@@ -1,4 +0,0 @@
-// Learn more https://docs.expo.io/guides/customizing-metro
-const { getDefaultConfig } = require('expo/metro-config');
-
-module.exports = getDefaultConfig(__dirname);
--- a/examples/bot-ready-signalling/client/react-native/package-lock.json
+++ b/examples/bot-ready-signalling/client/react-native/package-lock.json
--- a/examples/bot-ready-signalling/client/react-native/package.json
+++ b/examples/bot-ready-signalling/client/react-native/package.json
@@ -1,31 +0,0 @@
-{
-  "name": "bot-ready-rn",
-  "version": "1.0.0",
-  "scripts": {
-    "start": "expo start --dev-client",
-    "android": "expo run:android --device",
-    "ios": "expo run:ios --device",
-    "web": "expo start --web"
-  },
-  "dependencies": {
-    "@config-plugins/react-native-webrtc": "^10.0.0",
-    "@daily-co/config-plugin-rn-daily-js": "0.0.7",
-    "@daily-co/react-native-daily-js": "^0.70.0",
-    "@daily-co/react-native-webrtc": "^118.0.3-daily.2",
-    "@react-native-async-storage/async-storage": "1.23.1",
-    "expo": "^52.0.0",
-    "expo-build-properties": "~0.13.1",
-    "expo-dev-client": "~5.0.5",
-    "expo-splash-screen": "~0.29.16",
-    "expo-status-bar": "~2.0.0",
-    "react": "18.3.1",
-    "react-native": "0.76.3",
-    "react-native-background-timer": "^2.4.1",
-    "react-native-dotenv": "^3.4.11",
-    "react-native-get-random-values": "^1.11.0"
-  },
-  "devDependencies": {
-    "@babel/core": "^7.12.9"
-  },
-  "private": true
-}
--- a/examples/bot-ready-signalling/client/react-native/src/App.js
+++ b/examples/bot-ready-signalling/client/react-native/src/App.js
@@ -1,121 +0,0 @@
-import React, { useState, useEffect } from 'react';
-import {SafeAreaView, View, Text, Button, StyleSheet, ScrollView} from 'react-native';
-import Daily from "@daily-co/react-native-daily-js";
-import { API_BASE_URL } from "@env";
-
-const CallScreen = () => {
-  const [connectionStatus, setConnectionStatus] = useState('Disconnected');
-  const [isConnected, setIsConnected] = useState(false);
-  const [callObject, setCallObject] = useState(null);
-  const [logs, setLogs] = useState([]);
-
-  useEffect(() => {
-    if (callObject) {
-      setupTrackListeners(callObject);
-    }
-  }, [callObject]);
-
-  const log = (message) => {
-    setLogs((prevLogs) => [...prevLogs, `${new Date().toISOString()} - ${message}`]);
-    console.log(message);
-  };
-
-  const setupTrackListeners = (callObject) => {
-    callObject.on("joined-meeting", () => {
-      setConnectionStatus('Connected');
-      setIsConnected(true);
-      log('Client connected');
-    });
-    callObject.on("left-meeting", () => {
-      setConnectionStatus('Disconnected');
-      setIsConnected(false);
-      log('Client disconnected');
-    });
-    callObject.on("participant-left", () => {
-      // When the bot leaves, we are also disconnecting from the call
-      disconnect().catch((err) => {
-        log(`Failed to disconnect ${err}`);
-      })
-    });
-    // Trigger so the bot can start sending audio
-    callObject.on("track-started", (evt) => {
-      if (evt.track.kind === "audio" && evt.participant.local === false) {
-        handleEventToConsole(evt)
-        log("Sending the message that will trigger the bot to play the audio.")
-        callObject.sendAppMessage("playable")
-      }
-    });
-    callObject.on("error", (evt) => log(`Error: ${evt.error}`));
-    // Other events just for awareness
-    callObject.on("track-stopped", handleEventToConsole);
-    callObject.on("participant-joined", handleEventToConsole);
-    callObject.on("participant-updated", handleEventToConsole);
-  };
-
-  const handleEventToConsole = (evt) => {
-    log(`Received event: ${evt.action}`);
-  };
-
-  const connect = async () => {
-    try {
-      const callObject = Daily.createCallObject({ subscribeToTracksAutomatically: true });
-      setCallObject(callObject);
-      const connectionUrl = `${API_BASE_URL}/connect`
-      const res = await fetch(connectionUrl, { method: "POST", headers: { "Content-Type": "application/json" } });
-      const roomInfo = await res.json();
-      await callObject.join({ url: roomInfo.room_url });
-    } catch (error) {
-      log(`Error connecting: ${error.message}`);
-    }
-  };
-
-  const disconnect = async () => {
-    if (callObject) {
-      try {
-        await callObject.leave();
-        await callObject.destroy();
-        setCallObject(null);
-      } catch (error) {
-        log(`Error disconnecting: ${error.message}`);
-      }
-    }
-  };
-
-  return (
-      <SafeAreaView style={styles.safeArea}>
-        <View style={styles.container}>
-          <View style={styles.statusBar}>
-            <Text>Status: <Text style={styles.status}>{connectionStatus}</Text></Text>
-            <View style={styles.controls}>
-              <Button
-                title={isConnected ? "Disconnect" : "Connect"}
-                onPress={isConnected ? disconnect : connect}
-              />
-            </View>
-          </View>
-
-          <View style={styles.debugPanel}>
-            <Text style={styles.debugTitle}>Debug Info</Text>
-            <ScrollView style={styles.debugLog}>
-              {logs.map((logEntry, index) => (
-                  <Text key={index} style={styles.logText}>{logEntry}</Text>
-              ))}
-            </ScrollView>
-          </View>
-        </View>
-      </SafeAreaView>
-  );
-};
-
-const styles = StyleSheet.create({
-  safeArea: { flex: 1, backgroundColor: '#f0f0f0', padding: 20 },
-  container: { flex: 1, margin: 20 },
-  statusBar: { flexDirection: 'row', justifyContent: 'space-between', alignItems: 'center', padding: 10, backgroundColor: '#fff', borderRadius: 8, marginBottom: 20 },
-  status: { fontWeight: 'bold' },
-  controls: { flexDirection: 'row', gap: 10 },
-  debugPanel: { height: '80%', backgroundColor: '#fff', borderRadius: 8, padding: 20},
-  debugTitle: { fontSize: 16, fontWeight: 'bold' },
-  debugLog: { height: '100%', overflow: 'scroll', backgroundColor: '#f8f8f8', padding: 10, borderRadius: 4, fontFamily: 'monospace', fontSize: 12, lineHeight: 1.4 },
-});
-
-export default CallScreen;
--- a/examples/foundational/01a-local-audio.py
+++ b/examples/foundational/01a-local-audio.py
@@ -16,7 +16,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
+from pipecat.transports.local.audio import LocalAudioTransport, LocalTransportParams

 load_dotenv(override=True)

@@ -25,7 +25,7 @@ logger.add(sys.stderr, level="DEBUG")


 async def main():
-    transport = LocalAudioTransport(LocalAudioTransportParams(audio_out_enabled=True))
+    transport = LocalAudioTransport(LocalTransportParams(audio_out_enabled=True))

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
--- a/examples/foundational/06a-image-sync.py
+++ b/examples/foundational/06a-image-sync.py
@@ -27,7 +27,7 @@ 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.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport

@@ -91,7 +91,7 @@ async def main():
            ),
        )

-        tts = CartesiaTTSService(
+        tts = CartesiaHttpTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )
--- a/examples/foundational/07n-interruptible-google.py
+++ b/examples/foundational/07n-interruptible-google.py
@@ -18,7 +18,9 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.google import GoogleLLMService, GoogleSTTService, GoogleTTSService
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.google import GoogleTTSService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport

@@ -44,16 +46,14 @@ async def main():
            ),
        )

-        stt = GoogleSTTService(
-            params=GoogleSTTService.InputParams(languages=Language.EN_US),
-        )
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

        tts = GoogleTTSService(
            voice_id="en-US-Journey-F",
            params=GoogleTTSService.InputParams(language=Language.EN_US),
        )

-        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

        messages = [
            {
--- a/examples/foundational/07q-interruptible-rime.py
+++ b/examples/foundational/07q-interruptible-rime.py
@@ -19,7 +19,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
-from pipecat.services.rime import RimeTTSService
+from pipecat.services.rime import RimeHttpTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport

 load_dotenv(override=True)
@@ -44,9 +44,10 @@ async def main():
            ),
        )

-        tts = RimeTTSService(
+        tts = RimeHttpTTSService(
            api_key=os.getenv("RIME_API_KEY", ""),
            voice_id="rex",
+            params=RimeHttpTTSService.InputParams(reduce_latency=True),
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
--- a/examples/foundational/13a-whisper-local.py
+++ b/examples/foundational/13a-whisper-local.py
@@ -16,7 +16,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.whisper import WhisperSTTService
-from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
+from pipecat.transports.local.audio import LocalAudioTransport, LocalTransportParams

 load_dotenv(override=True)

@@ -33,7 +33,7 @@ class TranscriptionLogger(FrameProcessor):


 async def main():
-    transport = LocalAudioTransport(LocalAudioTransportParams(audio_in_enabled=True))
+    transport = LocalAudioTransport(LocalTransportParams(audio_in_enabled=True))

    stt = WhisperSTTService()

--- a/examples/foundational/14e-function-calling-gemini.py
+++ b/examples/foundational/14e-function-calling-gemini.py
@@ -14,7 +14,6 @@ from loguru import logger
 from runner import configure

 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -31,12 +30,6 @@ logger.add(sys.stderr, level="DEBUG")
 video_participant_id = None


-async def start_fetch_weather(function_name, llm, context):
-    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
-
-
 async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
    location = arguments["location"]
    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
@@ -70,7 +63,7 @@ async def main():
        )

        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-        llm.register_function("get_weather", get_weather, start_fetch_weather)
+        llm.register_function("get_weather", get_weather)
        llm.register_function("get_image", get_image)

        tools = [
--- a/examples/foundational/18-gstreamer-filesrc.py
+++ b/examples/foundational/18-gstreamer-filesrc.py
@@ -38,6 +38,7 @@ async def main():
            "GStreamer",
            DailyParams(
                audio_out_enabled=True,
+                audio_out_is_live=True,
                camera_out_enabled=True,
                camera_out_width=1280,
                camera_out_height=720,
--- a/examples/foundational/22d-natural-conversation-gemini-audio.py
+++ b/examples/foundational/22d-natural-conversation-gemini-audio.py
@@ -497,7 +497,7 @@ class UserAggregatorBuffer(LLMResponseAggregator):
        if isinstance(frame, UserStartedSpeakingFrame):
            self._transcription = ""

-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if self._aggregation:
            self._transcription = self._aggregation
            self._aggregation = ""
--- a/examples/foundational/24-stt-mute-filter.py
+++ b/examples/foundational/24-stt-mute-filter.py
@@ -62,10 +62,7 @@ async def main():
        # Configure the mute processor with both strategies
        stt_mute_processor = STTMuteFilter(
            config=STTMuteConfig(
-                strategies={
-                    STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE,
-                    STTMuteStrategy.FUNCTION_CALL,
-                }
+                strategies={STTMuteStrategy.FIRST_SPEECH, STTMuteStrategy.FUNCTION_CALL}
            ),
        )

--- a/examples/foundational/25-google-audio-in.py
+++ b/examples/foundational/25-google-audio-in.py
@@ -143,14 +143,7 @@ class InputTranscriptionContextFilter(FrameProcessor):
            return

        try:
-            # Make sure we're working with a GoogleLLMContext
-            context = GoogleLLMContext.upgrade_to_google(frame.context)
-            message = context.messages[-1]
-
-            if not isinstance(message, glm.Content):
-                logger.error(f"Expected glm.Content, got {type(message)}")
-                return
-
+            message = frame.context.messages[-1]
            last_part = message.parts[-1]
            if not (
                message.role == "user"
--- a/examples/foundational/30-observer.py
+++ b/examples/foundational/30-observer.py
@@ -18,10 +18,12 @@ from pipecat.frames.frames import (
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
    Frame,
+    LLMFullResponseEndFrame,
+    LLMFullResponseStartFrame,
+    LLMTextFrame,
    StartInterruptionFrame,
 )
 from pipecat.observers.base_observer import BaseObserver
-from pipecat.observers.loggers.llm_log_observer import LLMLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -71,6 +73,38 @@ class DebugObserver(BaseObserver):
            logger.info(f"🤖 BOT STOP SPEAKING: {src} {arrow} {dst} at {time_sec:.2f}s")


+class LLMLogObserver(BaseObserver):
+    """Observer to log LLM activity to the console.
+
+    Logs all frame instances of:
+    - LLMFullResponseStartFrame (only from LLM service)
+    - LLMTextFrame
+    - LLMFullResponseEndFrame (only from LLM service)
+
+    This allows you to track when the LLM starts responding, what it generates, and when it finishes.
+    Log format: [LLM EVENT]: [details] at [timestamp]s
+    """
+
+    async def on_push_frame(
+        self,
+        src: FrameProcessor,
+        dst: FrameProcessor,
+        frame: Frame,
+        direction: FrameDirection,
+        timestamp: int,
+    ):
+        time_sec = timestamp / 1_000_000_000
+
+        # Only log start/end frames from OpenAILLMService
+        if isinstance(frame, (LLMFullResponseStartFrame, LLMFullResponseEndFrame)):
+            if isinstance(src, OpenAILLMService):
+                event = "START" if isinstance(frame, LLMFullResponseStartFrame) else "END"
+                logger.info(f"🧠 LLM {event} RESPONSE at {time_sec:.2f}s")
+        # Log all LLMTextFrames
+        elif isinstance(frame, LLMTextFrame):
+            logger.info(f"🧠 LLM GENERATING: {frame.text!r} at {time_sec:.2f}s")
+
+
 async def main():
    async with aiohttp.ClientSession() as session:
        (room_url, token) = await configure(session)
--- a/examples/instant-voice/.gitignore
+++ b/examples/instant-voice/.gitignore
@@ -1,7 +0,0 @@
-venv
-.idea
-server/.env
-client/javascript/node_modules
-client/javascript/.vite
-bkp
-
--- a/examples/instant-voice/README.md
+++ b/examples/instant-voice/README.md
@@ -1,55 +0,0 @@
-# Instant Voice
-
-This demo showcases how to enable instant voice communication as soon as a user connects.
-By leveraging optimizations on both the server and client sides, users can start speaking immediately after pressing the connect button.
-
-## How It Works
-
-### Server-Side Improvements:
- A **pool of Daily rooms** is managed to ensure quick connections.
- When a user connects, an existing room from the pool is assigned.
- A new room is created asynchronously to maintain the predefined pool size.
-
-### Client-Side Improvements:
- Using the **DailyTransport** property `bufferLocalAudioUntilBotReady` set to enabled, users can start speaking immediately
-  upon receiving the `AUDIO_BUFFERING_STARTED` event (typically within ~1s).
- This allows users to speak even before the bot is fully ready or the WebRTC connection is fully established.
-
-## Quick Start
-
-### 1. Start the Bot Server
-
-1. Navigate to the server directory:
-   ```bash
-   cd server
-   ```
-2. Create and activate a virtual environment:
-   ```bash
-   python3 -m venv venv
-   source venv/bin/activate  # On Windows: venv\Scripts\activate
-   ```
-3. Install dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-4. Copy the `.env.example` file to `.env` and configure it:
-    - Add your API keys.
-5. Start the server:
-   ```bash
-   python src/server.py
-   ```
-
-### 2. Connect Using the Client App
-
-For client-side setup, refer to the [JavaScript Guide](client/javascript/README.md).
-
-## Important Notes
- The bot server **must** be running before using the client implementation.
- Ensure your environment variables are correctly set up.
-
-## Requirements
- **Python 3.10+**
- **Node.js 16+** (for JavaScript/React client)
- **Daily API key**
- **Google API key**
- **Modern web browser with WebRTC support**
--- a/examples/instant-voice/client/javascript/README.md
+++ b/examples/instant-voice/client/javascript/README.md
@@ -1,27 +0,0 @@
-# JavaScript Implementation
-
-Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
-
-## Setup
-
-1. Run the bot server. See the [server README](../../README).
-
-2. Navigate to the `client/javascript` directory:
-
-```bash
-cd client/javascript
-```
-
-3. Install dependencies:
-
-```bash
-npm install
-```
-
-4. Run the client app:
-
-```
-npm run dev
-```
-
-5. Visit http://localhost:5173 in your browser.
--- a/examples/instant-voice/client/javascript/index.html
+++ b/examples/instant-voice/client/javascript/index.html
@@ -1,37 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-  <meta charset="UTF-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  <title>AI Chatbot</title>
-</head>
-
-<body>
-<div class="container">
-  <div class="status-bar">
-    <div class="status">
-      Buffering audio: <span id="buffering-status">No</span>
-    </div>
-    <div class="status">
-      Transport: <span id="connection-status">Disconnected</span>
-    </div>
-    <div class="controls">
-      <button id="connect-btn">Connect</button>
-      <button id="disconnect-btn" disabled>Disconnect</button>
-    </div>
-  </div>
-
-  <audio id="bot-audio" autoplay></audio>
-
-  <div class="debug-panel">
-    <h3>Debug Info</h3>
-    <div id="debug-log"></div>
-  </div>
-</div>
-
-<script type="module" src="/src/app.ts"></script>
-<link rel="stylesheet" href="/src/style.css">
-</body>
-
-</html>
--- a/examples/instant-voice/client/javascript/package-lock.json
+++ b/examples/instant-voice/client/javascript/package-lock.json
--- a/examples/instant-voice/client/javascript/package.json
+++ b/examples/instant-voice/client/javascript/package.json
@@ -1,24 +0,0 @@
-{
-  "name": "client",
-  "version": "1.0.0",
-  "main": "index.js",
-  "scripts": {
-    "dev": "vite",
-    "build": "tsc && vite build",
-    "preview": "vite preview"
-  },
-  "keywords": [],
-  "author": "",
-  "license": "ISC",
-  "description": "",
-  "devDependencies": {
-    "@types/node": "^22.13.1",
-    "@vitejs/plugin-react-swc": "^3.7.2",
-    "typescript": "^5.7.3",
-    "vite": "^6.0.2"
-  },
-  "dependencies": {
-    "@pipecat-ai/client-js": "^0.3.2",
-    "@pipecat-ai/daily-transport": "^0.3.5"
-  }
-}
--- a/examples/instant-voice/client/javascript/src/app.ts
+++ b/examples/instant-voice/client/javascript/src/app.ts
@@ -1,268 +0,0 @@
-/**
- * Copyright (c) 2024–2025, Daily
- *
- * SPDX-License-Identifier: BSD 2-Clause License
- */
-
-/**
- * RTVI Client Implementation
- *
- * This client connects to an RTVI-compatible bot server using WebRTC (via Daily).
- * It handles audio/video streaming and manages the connection lifecycle.
- *
- * Requirements:
- * - A running RTVI bot server (defaults to http://localhost:7860)
- * - The server must implement the /connect endpoint that returns Daily.co room credentials
- * - Browser with WebRTC support
- */
-
-import {
-  Participant,
-  RTVIClient,
-  RTVIClientOptions,
-  RTVIEvent,
-} from '@pipecat-ai/client-js';
-import { DailyTransport } from '@pipecat-ai/daily-transport';
-import SoundUtils from "./util/soundUtils";
-import { InstantVoiceHelper } from "./util/instantVoiceHelper";
-
-/**
- * InstantVoiceClient handles the connection and media management for a real-time
- * voice and video interaction with an AI bot.
- */
-class InstantVoiceClient {
-
-  private declare rtviClient: RTVIClient;
-  private connectBtn: HTMLButtonElement | null = null;
-  private disconnectBtn: HTMLButtonElement | null = null;
-  private statusSpan: HTMLElement | null = null;
-  private bufferingAudioSpan: HTMLElement | null = null;
-  private debugLog: HTMLElement | null = null;
-  private botAudio: HTMLAudioElement;
-  private declare startTime: number;
-
-  constructor() {
-    this.botAudio = document.createElement('audio');
-    this.botAudio.autoplay = true;
-    document.body.appendChild(this.botAudio);
-    this.setupDOMElements();
-    this.setupEventListeners();
-    this.initializeRTVIClient();
-  }
-
-  /**
-   * Set up references to DOM elements and create necessary media elements
-   */
-  private setupDOMElements(): void {
-    this.connectBtn = document.getElementById('connect-btn') as HTMLButtonElement;
-    this.disconnectBtn = document.getElementById('disconnect-btn') as HTMLButtonElement;
-    this.statusSpan = document.getElementById('connection-status');
-    this.bufferingAudioSpan = document.getElementById('buffering-status');
-    this.debugLog = document.getElementById('debug-log');
-  }
-
-  /**
-   * Set up event listeners for connect/disconnect buttons
-   */
-  private setupEventListeners(): void {
-    this.connectBtn?.addEventListener('click', () => this.connect());
-    this.disconnectBtn?.addEventListener('click', () => this.disconnect());
-  }
-
-  private initializeRTVIClient(): void {
-    const transport = new DailyTransport({
-      bufferLocalAudioUntilBotReady: true
-    });
-    const RTVIConfig: RTVIClientOptions = {
-      transport,
-      params: {
-        // The baseURL and endpoint of your bot server that the client will connect to
-        baseUrl: 'http://localhost:7860',
-        endpoints: { connect: '/connect' },
-      },
-      enableMic: true,
-      enableCam: false,
-      callbacks: {
-        onConnected: () => {
-          this.updateStatus('Connected');
-          if (this.connectBtn) this.connectBtn.disabled = true;
-          if (this.disconnectBtn) this.disconnectBtn.disabled = false;
-        },
-        onDisconnected: () => {
-          this.updateStatus('Disconnected');
-          this.updateBufferingStatus('No');
-          if (this.connectBtn) this.connectBtn.disabled = false;
-          if (this.disconnectBtn) this.disconnectBtn.disabled = true;
-          this.log('Client disconnected');
-        },
-        onBotConnected: (participant: Participant)  => {
-          this.log(`onBotConnected, timeTaken: ${Date.now() - this.startTime}`);
-        },
-        onBotReady: (data) => {
-          this.log(`onBotReady, timeTaken: ${Date.now() - this.startTime}`);
-          this.log(`Bot ready: ${JSON.stringify(data)}`);
-          this.setupMediaTracks();
-        },
-        onUserTranscript: (data) => {
-          if (data.final) {
-            this.log(`User: ${data.text}`);
-          }
-        },
-        onBotTranscript: (data) => this.log(`Bot: ${data.text}`),
-        onMessageError: (error) => console.error('Message error:', error),
-        onError: (error) => console.error('Error:', error),
-      },
-    }
-
-    this.rtviClient = new RTVIClient(RTVIConfig);
-    this.rtviClient.registerHelper("transport", new InstantVoiceHelper({
-          callbacks: {
-            onAudioBufferingStarted: () => {
-              SoundUtils.beep()
-              this.updateBufferingStatus('Yes');
-              this.log(`onMicCaptureStarted, timeTaken: ${Date.now() - this.startTime}`);
-            },
-            onAudioBufferingStopped: () => {
-              this.updateBufferingStatus('No');
-              this.log(`onMicCaptureStopped, timeTaken: ${Date.now() - this.startTime}`);
-            }
-          }
-        }
-    ));
-    this.setupTrackListeners();
-  }
-
-  /**
-   * Add a timestamped message to the debug log
-   */
-  private log(message: string): void {
-    if (!this.debugLog) return;
-    const entry = document.createElement('div');
-    entry.textContent = `${new Date().toISOString()} - ${message}`;
-    if (message.startsWith('User: ')) {
-      entry.style.color = '#2196F3';
-    } else if (message.startsWith('Bot: ')) {
-      entry.style.color = '#4CAF50';
-    }
-    this.debugLog.appendChild(entry);
-    this.debugLog.scrollTop = this.debugLog.scrollHeight;
-    console.log(message);
-  }
-
-  /**
-   * Update the connection status display
-   */
-  private updateStatus(status: string): void {
-    if (this.statusSpan) {
-      this.statusSpan.textContent = status;
-    }
-    this.log(`Status: ${status}`);
-  }
-
-  /**
-   * Update the connection status display
-   */
-  private updateBufferingStatus(status: string): void {
-    if (this.bufferingAudioSpan) {
-      this.bufferingAudioSpan.textContent = status;
-    }
-    this.log(`BufferingStatus: ${status}`);
-  }
-
-  /**
-   * Check for available media tracks and set them up if present
-   * This is called when the bot is ready or when the transport state changes to ready
-   */
-  setupMediaTracks() {
-    if (!this.rtviClient) return;
-    const tracks = this.rtviClient.tracks();
-    if (tracks.bot?.audio) {
-      this.setupAudioTrack(tracks.bot.audio);
-    }
-  }
-
-  /**
-   * Set up listeners for track events (start/stop)
-   * This handles new tracks being added during the session
-   */
-  setupTrackListeners() {
-    if (!this.rtviClient) return;
-
-    // Listen for new tracks starting
-    this.rtviClient.on(RTVIEvent.TrackStarted, (track, participant) => {
-      // Only handle non-local (bot) tracks
-      if (!participant?.local && track.kind === 'audio') {
-        this.setupAudioTrack(track);
-      }
-    });
-
-    // Listen for tracks stopping
-    this.rtviClient.on(RTVIEvent.TrackStopped, (track, participant) => {
-      this.log(`Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`);
-    });
-  }
-
-  /**
-   * Set up an audio track for playback
-   * Handles both initial setup and track updates
-   */
-  private setupAudioTrack(track: MediaStreamTrack): void {
-    this.log('Setting up audio track');
-    if (this.botAudio.srcObject && "getAudioTracks" in this.botAudio.srcObject) {
-      const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
-      if (oldTrack?.id === track.id) return;
-    }
-    this.botAudio.srcObject = new MediaStream([track]);
-  }
-
-  /**
-   * Initialize and connect to the bot
-   * This sets up the RTVI client, initializes devices, and establishes the connection
-   */
-  public async connect(): Promise<void> {
-    try {
-      this.startTime = Date.now();
-      this.log('Connecting to bot...');
-      await this.rtviClient.connect();
-    } catch (error) {
-      this.log(`Error connecting: ${(error as Error).message}`);
-      this.updateStatus('Error');
-      this.updateBufferingStatus('No');
-
-      // Clean up if there's an error
-      if (this.rtviClient) {
-        try {
-          await this.rtviClient.disconnect();
-        } catch (disconnectError) {
-          this.log(`Error during disconnect: ${disconnectError}`);
-        }
-      }
-    }
-  }
-
-  /**
-   * Disconnect from the bot and clean up media resources
-   */
-  public async disconnect(): Promise<void> {
-    try {
-      await this.rtviClient.disconnect();
-      if (this.botAudio.srcObject && "getAudioTracks" in this.botAudio.srcObject) {
-        this.botAudio.srcObject.getAudioTracks().forEach((track) => track.stop());
-        this.botAudio.srcObject = null;
-      }
-    } catch (error) {
-      this.log(`Error disconnecting: ${(error as Error).message}`);
-    }
-  }
-}
-
-declare global {
-  interface Window {
-    InstantVoiceClient: typeof InstantVoiceClient;
-  }
-}
-
-window.addEventListener('DOMContentLoaded', () => {
-  window.InstantVoiceClient = InstantVoiceClient;
-  new InstantVoiceClient();
-});
--- a/examples/instant-voice/client/javascript/src/style.css
+++ b/examples/instant-voice/client/javascript/src/style.css
@@ -1,98 +0,0 @@
-body {
-    margin: 0;
-    padding: 20px;
-    font-family: Arial, sans-serif;
-    background-color: #f0f0f0;
-}
-
-.container {
-    max-width: 1200px;
-    margin: 0 auto;
-}
-
-.status-bar {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    padding: 10px;
-    background-color: #fff;
-    border-radius: 8px;
-    margin-bottom: 20px;
-}
-
-.controls button {
-    padding: 8px 16px;
-    margin-left: 10px;
-    border: none;
-    border-radius: 4px;
-    cursor: pointer;
-}
-
-#connect-btn {
-    background-color: #4caf50;
-    color: white;
-}
-
-#disconnect-btn {
-    background-color: #f44336;
-    color: white;
-}
-
-button:disabled {
-    opacity: 0.5;
-    cursor: not-allowed;
-}
-
-.main-content {
-    background-color: #fff;
-    border-radius: 8px;
-    padding: 20px;
-    margin-bottom: 20px;
-}
-
-.bot-container {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-}
-
-#bot-video-container {
-    width: 640px;
-    height: 360px;
-    background-color: #e0e0e0;
-    border-radius: 8px;
-    margin: 20px auto;
-    overflow: hidden;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-}
-
-#bot-video-container video {
-    width: 100%;
-    height: 100%;
-    object-fit: cover;
-}
-
-.debug-panel {
-    background-color: #fff;
-    border-radius: 8px;
-    padding: 20px;
-}
-
-.debug-panel h3 {
-    margin: 0 0 10px 0;
-    font-size: 16px;
-    font-weight: bold;
-}
-
-#debug-log {
-    height: 500px;
-    overflow-y: auto;
-    background-color: #f8f8f8;
-    padding: 10px;
-    border-radius: 4px;
-    font-family: monospace;
-    font-size: 12px;
-    line-height: 1.4;
-}
--- a/examples/instant-voice/client/javascript/src/util/instantVoiceHelper.ts
+++ b/examples/instant-voice/client/javascript/src/util/instantVoiceHelper.ts
@@ -1,39 +0,0 @@
-import {RTVIClientHelper, RTVIClientHelperOptions, RTVIMessage} from "@pipecat-ai/client-js";
-import {DailyRTVIMessageType} from '@pipecat-ai/daily-transport';
-
-export type InstantVoiceHelperCallbacks = Partial<{
-    onAudioBufferingStarted: () => void;
-    onAudioBufferingStopped: () => void;
-}>;
-
-// --- Interface and class
-export interface InstantVoiceHelperOptions extends RTVIClientHelperOptions {
-    callbacks?: InstantVoiceHelperCallbacks;
-}
-export class InstantVoiceHelper extends RTVIClientHelper {
-
-    protected declare _options: InstantVoiceHelperOptions;
-
-    constructor(options: InstantVoiceHelperOptions) {
-        super(options);
-    }
-
-    handleMessage(rtviMessage: RTVIMessage): void {
-        switch (rtviMessage.type) {
-            case DailyRTVIMessageType.AUDIO_BUFFERING_STARTED:
-                if (this._options.callbacks?.onAudioBufferingStarted) {
-                    this._options.callbacks?.onAudioBufferingStarted()
-                }
-                break;
-            case DailyRTVIMessageType.AUDIO_BUFFERING_STOPPED:
-                if (this._options.callbacks?.onAudioBufferingStopped) {
-                    this._options.callbacks?.onAudioBufferingStopped()
-                }
-                break;
-        }
-    }
-
-    getMessageTypes(): string[] {
-        return [DailyRTVIMessageType.AUDIO_BUFFERING_STARTED, DailyRTVIMessageType.AUDIO_BUFFERING_STOPPED];
-    }
-}
--- a/examples/instant-voice/client/javascript/src/util/soundUtils.ts
+++ b/examples/instant-voice/client/javascript/src/util/soundUtils.ts
@@ -1,8 +0,0 @@
-class SoundUtils {
-    static beep() {
-        const snd = new Audio("data:audio/wav;base64,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");
-        void snd.play();
-    }
-}
-
-export default SoundUtils;
--- a/examples/instant-voice/client/javascript/tsconfig.json
+++ b/examples/instant-voice/client/javascript/tsconfig.json
@@ -1,111 +0,0 @@
-{
-  "compilerOptions": {
-    /* Visit https://aka.ms/tsconfig to read more about this file */
-
-    /* Projects */
-    // "incremental": true,                              /* Save .tsbuildinfo files to allow for incremental compilation of projects. */
-    // "composite": true,                                /* Enable constraints that allow a TypeScript project to be used with project references. */
-    // "tsBuildInfoFile": "./.tsbuildinfo",              /* Specify the path to .tsbuildinfo incremental compilation file. */
-    // "disableSourceOfProjectReferenceRedirect": true,  /* Disable preferring source files instead of declaration files when referencing composite projects. */
-    // "disableSolutionSearching": true,                 /* Opt a project out of multi-project reference checking when editing. */
-    // "disableReferencedProjectLoad": true,             /* Reduce the number of projects loaded automatically by TypeScript. */
-
-    /* Language and Environment */
-    "target": "es2016",                                  /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */
-    // "lib": [],                                        /* Specify a set of bundled library declaration files that describe the target runtime environment. */
-    // "jsx": "preserve",                                /* Specify what JSX code is generated. */
-    // "experimentalDecorators": true,                   /* Enable experimental support for legacy experimental decorators. */
-    // "emitDecoratorMetadata": true,                    /* Emit design-type metadata for decorated declarations in source files. */
-    // "jsxFactory": "",                                 /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */
-    // "jsxFragmentFactory": "",                         /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */
-    // "jsxImportSource": "",                            /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */
-    // "reactNamespace": "",                             /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */
-    // "noLib": true,                                    /* Disable including any library files, including the default lib.d.ts. */
-    // "useDefineForClassFields": true,                  /* Emit ECMAScript-standard-compliant class fields. */
-    // "moduleDetection": "auto",                        /* Control what method is used to detect module-format JS files. */
-
-    /* Modules */
-    "module": "commonjs",                                /* Specify what module code is generated. */
-    // "rootDir": "./",                                  /* Specify the root folder within your source files. */
-    // "moduleResolution": "node10",                     /* Specify how TypeScript looks up a file from a given module specifier. */
-    // "baseUrl": "./",                                  /* Specify the base directory to resolve non-relative module names. */
-    // "paths": {},                                      /* Specify a set of entries that re-map imports to additional lookup locations. */
-    // "rootDirs": [],                                   /* Allow multiple folders to be treated as one when resolving modules. */
-    // "typeRoots": [],                                  /* Specify multiple folders that act like './node_modules/@types'. */
-    // "types": [],                                      /* Specify type package names to be included without being referenced in a source file. */
-    // "allowUmdGlobalAccess": true,                     /* Allow accessing UMD globals from modules. */
-    // "moduleSuffixes": [],                             /* List of file name suffixes to search when resolving a module. */
-    // "allowImportingTsExtensions": true,               /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */
-    // "rewriteRelativeImportExtensions": true,          /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */
-    // "resolvePackageJsonExports": true,                /* Use the package.json 'exports' field when resolving package imports. */
-    // "resolvePackageJsonImports": true,                /* Use the package.json 'imports' field when resolving imports. */
-    // "customConditions": [],                           /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */
-    // "noUncheckedSideEffectImports": true,             /* Check side effect imports. */
-    // "resolveJsonModule": true,                        /* Enable importing .json files. */
-    // "allowArbitraryExtensions": true,                 /* Enable importing files with any extension, provided a declaration file is present. */
-    // "noResolve": true,                                /* Disallow 'import's, 'require's or '<reference>'s from expanding the number of files TypeScript should add to a project. */
-
-    /* JavaScript Support */
-    // "allowJs": true,                                  /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */
-    // "checkJs": true,                                  /* Enable error reporting in type-checked JavaScript files. */
-    // "maxNodeModuleJsDepth": 1,                        /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */
-
-    /* Emit */
-    // "declaration": true,                              /* Generate .d.ts files from TypeScript and JavaScript files in your project. */
-    // "declarationMap": true,                           /* Create sourcemaps for d.ts files. */
-    // "emitDeclarationOnly": true,                      /* Only output d.ts files and not JavaScript files. */
-    // "sourceMap": true,                                /* Create source map files for emitted JavaScript files. */
-    // "inlineSourceMap": true,                          /* Include sourcemap files inside the emitted JavaScript. */
-    // "noEmit": true,                                   /* Disable emitting files from a compilation. */
-    // "outFile": "./",                                  /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */
-    // "outDir": "./",                                   /* Specify an output folder for all emitted files. */
-    // "removeComments": true,                           /* Disable emitting comments. */
-    // "importHelpers": true,                            /* Allow importing helper functions from tslib once per project, instead of including them per-file. */
-    // "downlevelIteration": true,                       /* Emit more compliant, but verbose and less performant JavaScript for iteration. */
-    // "sourceRoot": "",                                 /* Specify the root path for debuggers to find the reference source code. */
-    // "mapRoot": "",                                    /* Specify the location where debugger should locate map files instead of generated locations. */
-    // "inlineSources": true,                            /* Include source code in the sourcemaps inside the emitted JavaScript. */
-    // "emitBOM": true,                                  /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */
-    // "newLine": "crlf",                                /* Set the newline character for emitting files. */
-    // "stripInternal": true,                            /* Disable emitting declarations that have '@internal' in their JSDoc comments. */
-    // "noEmitHelpers": true,                            /* Disable generating custom helper functions like '__extends' in compiled output. */
-    // "noEmitOnError": true,                            /* Disable emitting files if any type checking errors are reported. */
-    // "preserveConstEnums": true,                       /* Disable erasing 'const enum' declarations in generated code. */
-    // "declarationDir": "./",                           /* Specify the output directory for generated declaration files. */
-
-    /* Interop Constraints */
-    // "isolatedModules": true,                          /* Ensure that each file can be safely transpiled without relying on other imports. */
-    // "verbatimModuleSyntax": true,                     /* Do not transform or elide any imports or exports not marked as type-only, ensuring they are written in the output file's format based on the 'module' setting. */
-    // "isolatedDeclarations": true,                     /* Require sufficient annotation on exports so other tools can trivially generate declaration files. */
-    // "allowSyntheticDefaultImports": true,             /* Allow 'import x from y' when a module doesn't have a default export. */
-    "esModuleInterop": true,                             /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */
-    // "preserveSymlinks": true,                         /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */
-    "forceConsistentCasingInFileNames": true,            /* Ensure that casing is correct in imports. */
-
-    /* Type Checking */
-    "strict": true,                                      /* Enable all strict type-checking options. */
-    // "noImplicitAny": true,                            /* Enable error reporting for expressions and declarations with an implied 'any' type. */
-    // "strictNullChecks": true,                         /* When type checking, take into account 'null' and 'undefined'. */
-    // "strictFunctionTypes": true,                      /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */
-    // "strictBindCallApply": true,                      /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */
-    // "strictPropertyInitialization": true,             /* Check for class properties that are declared but not set in the constructor. */
-    // "strictBuiltinIteratorReturn": true,              /* Built-in iterators are instantiated with a 'TReturn' type of 'undefined' instead of 'any'. */
-    // "noImplicitThis": true,                           /* Enable error reporting when 'this' is given the type 'any'. */
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-    // "alwaysStrict": true,                             /* Ensure 'use strict' is always emitted. */
-    // "noUnusedLocals": true,                           /* Enable error reporting when local variables aren't read. */
-    // "noUnusedParameters": true,                       /* Raise an error when a function parameter isn't read. */
-    // "exactOptionalPropertyTypes": true,               /* Interpret optional property types as written, rather than adding 'undefined'. */
-    // "noImplicitReturns": true,                        /* Enable error reporting for codepaths that do not explicitly return in a function. */
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-    // "noUncheckedIndexedAccess": true,                 /* Add 'undefined' to a type when accessed using an index. */
-    // "noImplicitOverride": true,                       /* Ensure overriding members in derived classes are marked with an override modifier. */
-    // "noPropertyAccessFromIndexSignature": true,       /* Enforces using indexed accessors for keys declared using an indexed type. */
-    // "allowUnusedLabels": true,                        /* Disable error reporting for unused labels. */
-    // "allowUnreachableCode": true,                     /* Disable error reporting for unreachable code. */
-
-    /* Completeness */
-    // "skipDefaultLibCheck": true,                      /* Skip type checking .d.ts files that are included with TypeScript. */
-    "skipLibCheck": true                                 /* Skip type checking all .d.ts files. */
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--- a/examples/instant-voice/client/javascript/vite.config.js
+++ b/examples/instant-voice/client/javascript/vite.config.js
@@ -1,15 +0,0 @@
-import { defineConfig } from 'vite';
-import react from '@vitejs/plugin-react-swc';
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-                target: 'http://0.0.0.0:7860', // Replace with your backend URL
-                changeOrigin: true,
-            },
-        },
-    },
-});
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-  optionalDependencies:
-    rxjs "*"
-
-typescript@^5.7.3:
-  version "5.7.3"
-  resolved "https://registry.npmjs.org/typescript/-/typescript-5.7.3.tgz"
-  integrity sha512-84MVSjMEHP+FQRPy3pX9sTVV/INIex71s9TL2Gm5FG/WG1SqXeKyZ0k7/blY/4FdOzI12CBy1vGc4og/eus0fw==
-
-undici-types@~6.20.0:
-  version "6.20.0"
-  resolved "https://registry.npmjs.org/undici-types/-/undici-types-6.20.0.tgz"
-  integrity sha512-Ny6QZ2Nju20vw1SRHe3d9jVu6gJ+4e3+MMpqu7pqE5HT6WsTSlce++GQmK5UXS8mzV8DSYHrQH+Xrf2jVcuKNg==
-
-uuid@^10.0.0:
-  version "10.0.0"
-  resolved "https://registry.npmjs.org/uuid/-/uuid-10.0.0.tgz"
-  integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==
-
-"vite@^4 || ^5 || ^6", vite@^6.0.2:
-  version "6.0.2"
-  resolved "https://registry.npmjs.org/vite/-/vite-6.0.2.tgz"
-  integrity sha512-XdQ+VsY2tJpBsKGs0wf3U/+azx8BBpYRHFAyKm5VeEZNOJZRB63q7Sc8Iup3k0TrN3KO6QgyzFf+opSbfY1y0g==
-  dependencies:
-    esbuild "^0.24.0"
-    postcss "^8.4.49"
-    rollup "^4.23.0"
-  optionalDependencies:
-    fsevents "~2.3.3"
--- a/examples/instant-voice/server/env.example
+++ b/examples/instant-voice/server/env.example
@@ -1,3 +0,0 @@
-DAILY_API_KEY=
-GOOGLE_API_KEY=
-DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
--- a/examples/instant-voice/server/pyproject.toml
+++ b/examples/instant-voice/server/pyproject.toml
@@ -1,11 +0,0 @@
-[tool.ruff]
-exclude = [".git", "*_pb2.py"]
-line-length = 100
-
-[tool.ruff.lint]
-select = [
-    "I", # Import rules
-]
-
-[tool.ruff.lint.pydocstyle]
-convention = "google"
--- a/examples/instant-voice/server/requirements.txt
+++ b/examples/instant-voice/server/requirements.txt
@@ -1,4 +0,0 @@
-python-dotenv
-fastapi[all]
-uvicorn
-pipecat-ai[openai,silero,websocket,google,daily]
--- a/examples/instant-voice/server/src/server.py
+++ b/examples/instant-voice/server/src/server.py
@@ -1,200 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-import asyncio
-import os
-from contextlib import asynccontextmanager
-from typing import Any, Dict, List
-
-import aiohttp
-import uvicorn
-from dotenv import load_dotenv
-from fastapi import FastAPI, HTTPException, Request
-from fastapi.middleware.cors import CORSMiddleware
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
-
-# Load environment variables
-load_dotenv(override=True)
-
-NUMBER_OF_ROOMS = 1
-
-
-class RoomPool:
-    """Manages a pool of pre-created rooms for quick allocation."""
-
-    def __init__(self, daily_rest_helper: DailyRESTHelper):
-        self.daily_rest_helper = daily_rest_helper
-        self.pool: List[Dict[str, str]] = []
-        self.lock = asyncio.Lock()
-
-    async def fill_pool(self, count: int):
-        """Fills the pool with `count` new rooms."""
-        for _ in range(count):
-            await self.add_room()
-
-    async def add_room(self):
-        """Creates a new room and adds it to the pool."""
-        try:
-            room = await self.daily_rest_helper.create_room(DailyRoomParams())
-            if not room.url:
-                raise HTTPException(status_code=500, detail="Failed to create room")
-
-            user_token = await self.daily_rest_helper.get_token(room.url)
-            if not user_token:
-                raise HTTPException(status_code=500, detail="Failed to get user token")
-
-            bot_token = await self.daily_rest_helper.get_token(room.url)
-            if not bot_token:
-                raise HTTPException(status_code=500, detail="Failed to get bot token")
-
-            async with self.lock:
-                self.pool.append(
-                    {"room_url": room.url, "user_token": user_token, "bot_token": bot_token}
-                )
-
-        except Exception as e:
-            print(f"Error adding room to pool: {e}")
-
-    async def get_room(self) -> Dict[str, str]:
-        """Retrieves a room from the pool and requests a new one to maintain the size."""
-        async with self.lock:
-            if not self.pool:
-                raise HTTPException(status_code=503, detail="No available rooms")
-
-            room = self.pool.pop(0)  # Get first available room
-
-        # Start a background task to replenish the pool
-        asyncio.create_task(self.add_room())
-
-        return room
-
-    async def delete_room(self, room_url: str):
-        """Deletes a room when it is not needed anymore"""
-        await self.daily_rest_helper.delete_room_by_url(room_url)
-
-    async def cleanup(self):
-        for rooms in self.pool:
-            room_url = rooms["room_url"]
-            await self.delete_room(room_url)
-
-
-class BotManager:
-    """Manages bot subprocesses asynchronously."""
-
-    def __init__(self):
-        self.bot_procs: Dict[int, asyncio.subprocess.Process] = {}
-        self.room_mappings: Dict[int, str] = {}  # Maps process ID to room URL
-
-    async def start_bot(self, room_url: str, token: str) -> int:
-        bot_file = "single_bot"
-        command = f"python3 -m {bot_file} -u {room_url} -t {token}"
-
-        try:
-            proc = await asyncio.create_subprocess_shell(
-                command,
-                cwd=os.path.dirname(os.path.abspath(__file__)),
-            )
-            if proc.pid is None:
-                raise HTTPException(status_code=500, detail="Failed to get subprocess PID")
-
-            self.bot_procs[proc.pid] = proc
-            self.room_mappings[proc.pid] = room_url
-            # Monitor the process and delete the room when it exits
-            asyncio.create_task(self._monitor_process(proc.pid))
-
-            return proc.pid
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
-
-    async def _monitor_process(self, pid: int):
-        """Monitors a bot process and deletes the associated room when it exits."""
-        proc = self.bot_procs.get(pid)
-        if proc:
-            await proc.wait()  # Wait for the process to exit
-            room_url = self.room_mappings.pop(pid, None)
-
-            if room_url:
-                await room_pool.delete_room(room_url)
-                print(f"Deleted room: {room_url}")
-
-            del self.bot_procs[pid]
-
-    async def cleanup(self):
-        """Terminates all running bot processes and deletes associated rooms."""
-        for pid, proc in list(self.bot_procs.items()):
-            try:
-                proc.terminate()
-                await asyncio.wait_for(proc.wait(), timeout=5)
-
-                room_url = self.room_mappings.pop(pid, None)
-                if room_url:
-                    await room_pool.delete_room(room_url)  # Delete room when process terminates
-                    print(f"Deleted room: {room_url}")
-
-            except asyncio.TimeoutError:
-                print(f"Process {pid} did not terminate in time.")
-            except Exception as e:
-                print(f"Error terminating process {pid}: {e}")
-
-        # Clear remaining mappings
-        self.bot_procs.clear()
-        self.room_mappings.clear()
-
-
-# Global instances
-bot_manager = BotManager()
-room_pool: RoomPool  # Will be initialized in lifespan
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    """Handles FastAPI startup and shutdown."""
-    global room_pool
-    aiohttp_session = aiohttp.ClientSession()
-    daily_rest_helper = DailyRESTHelper(
-        daily_api_key=os.getenv("DAILY_API_KEY", ""),
-        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-        aiohttp_session=aiohttp_session,
-    )
-
-    room_pool = RoomPool(daily_rest_helper)
-    await room_pool.fill_pool(NUMBER_OF_ROOMS)  # Fill pool on startup
-
-    yield  # Run app
-
-    await bot_manager.cleanup()
-    await room_pool.cleanup()
-    await aiohttp_session.close()
-
-
-# Initialize FastAPI app with lifespan manager
-app = FastAPI(lifespan=lifespan)
-
-# Configure CORS to allow requests from any origin
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.post("/connect")
-async def bot_connect(request: Request) -> Dict[Any, Any]:
-    try:
-        room = await room_pool.get_room()
-        await bot_manager.start_bot(room["room_url"], room["bot_token"])
-    except HTTPException as e:
-        return {"error": str(e)}
-
-    return {
-        "room_url": room["room_url"],
-        "token": room["user_token"],
-    }
-
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
--- a/examples/instant-voice/server/src/single_bot.py
+++ b/examples/instant-voice/server/src/single_bot.py
@@ -1,120 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-import argparse
-import asyncio
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor
-from pipecat.services.gemini_multimodal_live import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-SYSTEM_INSTRUCTION = f"""
-"You are Gemini Chatbot, a friendly, helpful robot.
-
-Your goal is to demonstrate your capabilities in a succinct way.
-
-Your output will be converted to audio so don't include special characters in your answers.
-
-Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most.
-"""
-
-
-def extract_arguments():
-    parser = argparse.ArgumentParser(description="Instant Voice Example")
-    parser.add_argument(
-        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-t", "--token", type=str, required=False, help="Token of the Daily room to join"
-    )
-    args, unknown = parser.parse_known_args()
-    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
-    token = args.token
-    return url, token
-
-
-async def main():
-    room_url, token = extract_arguments()
-    print(f"room_url: {room_url}")
-
-    daily_transport = DailyTransport(
-        room_url,
-        token,
-        "Instant voice Chatbot",
-        DailyParams(
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        ),
-    )
-
-    llm = GeminiMultimodalLiveLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
-        transcribe_user_audio=True,
-        transcribe_model_audio=True,
-        system_instruction=SYSTEM_INSTRUCTION,
-    )
-
-    context = OpenAILLMContext()
-    context_aggregator = llm.create_context_aggregator(context)
-
-    # RTVI events for Pipecat client UI
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]), transport=daily_transport)
-
-    pipeline = Pipeline(
-        [
-            daily_transport.input(),
-            context_aggregator.user(),
-            rtvi,
-            llm,  # LLM
-            daily_transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            observers=[rtvi.observer()],
-        ),
-    )
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-
-    @daily_transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @daily_transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        print(f"Participant left: {participant}")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/local-input-select-stt/bot.py
+++ b/examples/local-input-select-stt/bot.py
@@ -18,7 +18,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.whisper import Model, WhisperSTTService
-from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
+from pipecat.transports.local.audio import LocalAudioTransport, LocalTransportParams

 load_dotenv(override=True)

@@ -36,7 +36,7 @@ class TranscriptionLogger(FrameProcessor):

 async def main(input_device: int, output_device: int):
    transport = LocalAudioTransport(
-        LocalAudioTransportParams(
+        LocalTransportParams(
            audio_in_enabled=True,
            audio_out_enabled=False,
            input_device_index=input_device,
--- a/examples/phone-chatbot/README.md
+++ b/examples/phone-chatbot/README.md
@@ -1,5 +1,3 @@
-<!-- @format -->
-
 <div align="center">
  <img alt="pipecat" width="300px" height="auto" src="image.png">
 </div>
@@ -106,21 +104,6 @@ curl -X POST "http://localhost:7860/daily_start_bot" \
     -d '{"dialoutNumber": "+18057145330", "detectVoicemail": true}'
 ```

-### New! Using Gemini with Daily
-
-We have introduced a new example file that uses Gemini. You can find the code within bot_daily_gemini.py.
-If you want to spin up a Gemini-based bot for this demo, instead of an OpenAI-based bot, call the same properties above but on the `daily_gemini_start_bot` endpoint instead.
-
-For example:
-
-```shell
-curl -X POST "http://localhost:7860/daily_gemini_start_bot" \                                                                                                        py pipecat
-     -H "Content-Type: application/json" \
-     -d '{"detectVoicemail": true}'
-```
-
-Any request body properties supported by `/daily_start_bot` (such as "detectVoicemail", "dialoutnumber", etc) can also be passed to `/daily_gemini_start_bot`. The only difference is that calling the Gemini endpoint will start a Gemini bot session.
-
 ### More information

 For more configuration options, please consult [Daily's API documentation](https://docs.daily.co).
--- a/examples/phone-chatbot/bot_daily.py
+++ b/examples/phone-chatbot/bot_daily.py
@@ -98,7 +98,6 @@ async def main(
            - **"Record your message after the tone."**
            - **Any phrase that suggests an answering machine or voicemail.**
            - **ASSUME IT IS A VOICEMAIL. DO NOT WAIT FOR MORE CONFIRMATION.**
-            - **IF THE CALL SAYS "PLEASE LEAVE A MESSAGE AFTER THE BEEP", WAIT FOR THE BEEP BEFORE LEAVING A MESSAGE.**

            #### **Step 2: Leave a Voicemail Message**
            - Immediately say:
@@ -111,9 +110,7 @@ async def main(
            - If the call is answered by a human, say:
            *"Oh, hello! I'm a friendly chatbot. Is there anything I can help you with?"*
            - Keep responses **brief and helpful**.
-            - If the user no longer needs assistance, say:
-            *"Okay, thank you! Have a great day!"*
-            -**Then call `terminate_call` immediately.**
+            - If the user no longer needs assistance, **call `terminate_call` immediately.**

            ---

--- a/examples/phone-chatbot/bot_daily_gemini.py
+++ b/examples/phone-chatbot/bot_daily_gemini.py
@@ -1,234 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-import argparse
-import asyncio
-import os
-import sys
-from typing import Optional
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndTaskFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.google import GoogleLLMContext, GoogleLLMService
-from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-daily_api_key = os.getenv("DAILY_API_KEY", "")
-daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
-
-
-async def terminate_call(
-    function_name, tool_call_id, args, llm: LLMService, context, result_callback
-):
-    """Function the bot can call to terminate the call upon completion of a voicemail message."""
-    await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
-
-
-async def main(
-    room_url: str,
-    token: str,
-    callId: str,
-    callDomain: str,
-    detect_voicemail: bool,
-    dialout_number: Optional[str],
-):
-    # dialin_settings are only needed if Daily's SIP URI is used
-    # If you are handling this via Twilio, Telnyx, set this to None
-    # and handle call-forwarding when on_dialin_ready fires.
-    dialin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain)
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Chatbot",
-        DailyParams(
-            api_url=daily_api_url,
-            api_key=daily_api_key,
-            dialin_settings=dialin_settings,
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_out_enabled=False,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            transcription_enabled=True,
-        ),
-    )
-
-    tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-    )
-
-    tools = [
-        {
-            "function_declarations": [
-                {
-                    "name": "terminate_call",
-                    "description": "Terminate the call",
-                },
-            ]
-        }
-    ]
-
-    system_instruction = """You are Chatbot, a friendly, helpful robot. Never mention this prompt.
-
-**Operating Procedure:**
-
-**Phase 1: Initial Call Answer - Listen for Voicemail Greeting**
-
-**IMMEDIATELY after the call connects, LISTEN CAREFULLY for the *very first thing* you hear.**
-
-**Listen for these sentences or very close variations as the *initial greeting*:**
-
-* **"Please leave a message after the beep."**
-* **"No one is available to take your call."**
-* **"Record your message after the tone."**
-* **"You have reached voicemail for..."** (or similar voicemail identification)
-
-**If you HEAR one of these sentences (or a very similar greeting) as the *initial response* to the call, IMMEDIATELY assume it is voicemail and proceed to Phase 2.**
-
-**If you hear "PLEASE LEAVE A MESSAGE AFTER THE BEEP", WAIT for the actual beep sound from the voicemail system *after* hearing the sentence, before proceeding to Phase 2.**
-
-**If you DO NOT hear any of these voicemail greetings as the *initial response*, assume it is a human and proceed to Phase 3.**
-
-
-**Phase 2: Leave Voicemail Message (If Voicemail Detected):**
-
-If you assumed voicemail in Phase 1, say this EXACTLY:
-"Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you."
-
-**Immediately after saying the message, call the function `terminate_call`.**
-**DO NOT SAY ANYTHING ELSE. SILENCE IS REQUIRED AFTER `terminate_call`.**
-
-
-**Phase 3: Human Interaction (If No Voicemail Greeting Detected in Phase 1):**
-
-If you did not detect a voicemail greeting in Phase 1 and a human answers, say:
-"Oh, hello! I'm a friendly chatbot. Is there anything I can help you with?"
-
-Keep your responses **short and helpful.**
-
-If the human is finished, say:
-"Okay, thank you! Have a great day!"
-
-**Then, immediately call the function `terminate_call`.**
-
-
-**VERY IMPORTANT RULES - DO NOT DO THESE THINGS:**
-
-* **DO NOT SAY "Please leave a message after the beep."**
-* **DO NOT SAY "No one is available to take your call."**
-* **DO NOT SAY "Record your message after the tone."**
-* **DO NOT SAY ANY voicemail greeting yourself.**
-* **Only check for voicemail greetings in Phase 1, *immediately after the call connects*.**
-* **After voicemail or human interaction, ALWAYS call `terminate_call` immediately.**
-* **Do not speak after calling `terminate_call`.**
-* Your speech will be audio, so use simple language without special characters.
-"""
-
-    llm = GoogleLLMService(
-        model="models/gemini-2.0-flash-exp",
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        system_instruction=system_instruction,
-        tools=tools,
-    )
-    llm.register_function("terminate_call", terminate_call)
-
-    context = GoogleLLMContext()
-
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        PipelineParams(allow_interruptions=True),
-    )
-
-    if dialout_number:
-        logger.debug("dialout number detected; doing dialout")
-
-        # Configure some handlers for dialing out
-        @transport.event_handler("on_joined")
-        async def on_joined(transport, data):
-            logger.debug(f"Joined; starting dialout to: {dialout_number}")
-            await transport.start_dialout({"phoneNumber": dialout_number})
-
-        @transport.event_handler("on_dialout_connected")
-        async def on_dialout_connected(transport, data):
-            logger.debug(f"Dial-out connected: {data}")
-
-        @transport.event_handler("on_dialout_answered")
-        async def on_dialout_answered(transport, data):
-            logger.debug(f"Dial-out answered: {data}")
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # unlike the dialin case, for the dialout case, the caller will speak first. Presumably
-            # they will answer the phone and say "Hello?" Since we've captured their transcript,
-            # That will put a frame into the pipeline and prompt an LLM completion, which is how the
-            # bot will then greet the user.
-    elif detect_voicemail:
-        logger.debug("Detect voicemail example. You can test this in example in Daily Prebuilt")
-
-        # For the voicemail detection case, we do not want the bot to answer the phone. We want it to wait for the voicemail
-        # machine to say something like 'Leave a message after the beep', or for the user to say 'Hello?'.
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-    else:
-        logger.debug("no dialout number; assuming dialin")
-
-        # Different handlers for dialin
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # For the dialin case, we want the bot to answer the phone and greet the user. We
-            # can prompt the bot to speak by putting the context into the pipeline.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot")
-    parser.add_argument("-u", type=str, help="Room URL")
-    parser.add_argument("-t", type=str, help="Token")
-    parser.add_argument("-i", type=str, help="Call ID")
-    parser.add_argument("-d", type=str, help="Call Domain")
-    parser.add_argument("-v", action="store_true", help="Detect voicemail")
-    parser.add_argument("-o", type=str, help="Dialout number", default=None)
-    config = parser.parse_args()
-
-    asyncio.run(main(config.u, config.t, config.i, config.d, config.v, config.o))
--- a/examples/phone-chatbot/bot_runner.py
+++ b/examples/phone-chatbot/bot_runner.py
@@ -110,15 +110,10 @@ async def _create_daily_room(

    # Spawn a new agent, and join the user session
    # Note: this is mostly for demonstration purposes (refer to 'deployment' in docs)
-    print(f"Vendor: {vendor}")
    if vendor == "daily":
        bot_proc = f"python3 -m bot_daily -u {room.url} -t {token} -i {callId} -d {callDomain}{' -v' if detect_voicemail else ''}"
        if dialoutNumber:
            bot_proc += f" -o {dialoutNumber}"
-    elif vendor == "daily-gemini":
-        bot_proc = f"python3 -m bot_daily_gemini -u {room.url} -t {token} -i {callId} -d {callDomain}{' -v' if detect_voicemail else ''}"
-        if dialoutNumber:
-            bot_proc += f" -o {dialoutNumber}"
    else:
        bot_proc = f"python3 -m bot_twilio -u {room.url} -t {token} -i {callId} -s {room.config.sip_endpoint}"

@@ -206,38 +201,6 @@ async def daily_start_bot(request: Request) -> JSONResponse:
    return JSONResponse({"room_url": room.url, "sipUri": room.config.sip_endpoint})


-@app.post("/daily_gemini_start_bot")
-async def daily_gemini_start_bot(request: Request) -> JSONResponse:
-    # The /daily_start_bot is invoked when a call is received on Daily's SIP URI
-    # daily_start_bot will create the room, put the call on hold until
-    # the bot and sip worker are ready. Daily will automatically
-    # forward the call to the SIP URi when dialin_ready fires.
-
-    # Use specified room URL, or create a new one if not specified
-    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", None)
-    # Get the dial-in properties from the request
-    try:
-        data = await request.json()
-        if "test" in data:
-            # Pass through any webhook checks
-            return JSONResponse({"test": True})
-        detect_voicemail = data.get("detectVoicemail", False)
-        callId = data.get("callId", None)
-        callDomain = data.get("callDomain", None)
-        dialoutNumber = data.get("dialoutNumber", None)
-    except Exception:
-        raise HTTPException(
-            status_code=500, detail="Missing properties 'callId', 'callDomain', or 'dialoutNumber'"
-        )
-
-    room: DailyRoomObject = await _create_daily_room(
-        room_url, callId, callDomain, dialoutNumber, "daily-gemini", detect_voicemail
-    )
-
-    # Grab a token for the user to join with
-    return JSONResponse({"room_url": room.url, "sipUri": room.config.sip_endpoint})
-
-
 # ----------------- Main ----------------- #


--- a/examples/translation-chatbot/bot.py
+++ b/examples/translation-chatbot/bot.py
@@ -24,15 +24,17 @@ 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.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.services.daily import (
+    DailyParams,
+    DailyTransport,
+)

 load_dotenv(override=True)

@@ -164,32 +166,21 @@ async def main():
        async def on_transcript_update(processor, frame):
            await transcript_handler.on_transcript_update(processor, frame)

-        rtvi = RTVIProcessor()
-
        pipeline = Pipeline(
            [
                transport.input(),
-                rtvi,
                stt,
                transcript.user(),  # User transcripts
                tp,
                llm,
                tts,
                transport.output(),
-                transcript.assistant(),
                context_aggregator.assistant(),
+                transcript.assistant(),  # Assistant transcripts
            ]
        )

-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=False,  # We don't want to interrupt the translator bot
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                observers=[RTVIObserver(rtvi)],
-            ),
-        )
+        task = PipelineTask(pipeline)

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/translation-chatbot/requirements.txt
+++ b/examples/translation-chatbot/requirements.txt
@@ -1,4 +1,4 @@
 python-dotenv
 fastapi[all]
-pipecat-ai[cartesia,daily,deepgram,openai,silero]
+pipecat-ai[daily,openai,azure]
 aiohttp
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,8 +33,7 @@ dependencies = [
    "pydantic~=2.10.5",
    "pyloudnorm~=0.1.1",
    "resampy~=0.4.3",
-    "soxr~=0.5.0",
-    "tenacity~=9.0.0"
+    "soxr~=0.5.0"
 ]

 [project.urls]
@@ -56,7 +55,7 @@ elevenlabs = [ "websockets~=13.1" ]
 fal = [ "fal-client~=0.5.6" ]
 fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
 gladia = [ "websockets~=13.1" ]
-google = [ "google-cloud-speech~=2.31.0", "google-cloud-texttospeech~=2.25.0", "google-genai~=1.2.0", "google-generativeai~=0.8.4" ]
+google = [ "google-generativeai~=0.8.3", "google-cloud-texttospeech~=2.24.0", "google-genai~=1.0.0" ]
 grok = [ "openai~=1.59.6" ]
 groq = [ "openai~=1.59.6" ]
 gstreamer = [ "pygobject~=3.50.0" ]
@@ -74,7 +73,6 @@ openai = [ "openai~=1.59.6", "websockets~=13.1", "python-deepcompare~=2.1.0" ]
 openpipe = [ "openpipe~=4.45.0" ]
 perplexity = [ "openai~=1.59.6" ]
 playht = [ "pyht~=0.1.6", "websockets~=13.1" ]
-rime = [ "websockets~=13.1" ]
 riva = [ "nvidia-riva-client~=2.18.0" ]
 sentry = [ "sentry-sdk~=2.20.0" ]
 silero = [ "onnxruntime~=1.20.1" ]
--- a/src/pipecat/frames/frames.py
+++ b/src/pipecat/frames/frames.py
@@ -565,22 +565,6 @@ class UserStoppedSpeakingFrame(SystemFrame):
    pass


-@dataclass
-class EmulateUserStartedSpeakingFrame(SystemFrame):
-    """Emitted by internal processors upstream to emulate VAD behavior when a
-    user starts speaking."""
-
-    pass
-
-
-@dataclass
-class EmulateUserStoppedSpeakingFrame(SystemFrame):
-    """Emitted by internal processors upstream to emulate VAD behavior when a
-    user stops speaking."""
-
-    pass
-
-
@dataclass
 class BotInterruptionFrame(SystemFrame):
    """Emitted by when the bot should be interrupted. This will mainly cause the
@@ -634,13 +618,6 @@ class FunctionCallInProgressFrame(SystemFrame):
    arguments: str


-@dataclass
-class STTMuteFrame(SystemFrame):
-    """System frame to mute/unmute the STT service."""
-
-    mute: bool
-
-
@dataclass
 class TransportMessageUrgentFrame(SystemFrame):
    message: Any
@@ -775,6 +752,13 @@ class TTSUpdateSettingsFrame(ServiceUpdateSettingsFrame):
    pass


+@dataclass
+class STTMuteFrame(ControlFrame):
+    """Control frame to mute/unmute the STT service."""
+
+    mute: bool
+
+
@dataclass
 class STTUpdateSettingsFrame(ServiceUpdateSettingsFrame):
    pass
--- a/src/pipecat/observers/loggers/init.py
+++ b/src/pipecat/observers/loggers/init.py
--- a/src/pipecat/observers/loggers/llm_log_observer.py
+++ b/src/pipecat/observers/loggers/llm_log_observer.py
@@ -1,85 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from loguru import logger
-
-from pipecat.frames.frames import (
-    Frame,
-    FunctionCallInProgressFrame,
-    FunctionCallResultFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMMessagesFrame,
-    LLMTextFrame,
-)
-from pipecat.observers.base_observer import BaseObserver
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.ai_services import LLMService
-
-
-class LLMLogObserver(BaseObserver):
-    """Observer to log LLM activity to the console.
-
-    Logs all frame instances (only from/to LLM service) of:
-
-    - LLMFullResponseStartFrame
-    - LLMFullResponseEndFrame
-    - LLMTextFrame
-    - FunctionCallInProgressFrame
-    - LLMMessagesFrame
-    - OpenAILLMContextFrame
-
-    This allows you to track when the LLM starts responding, what it generates,
-    and when it finishes.
-
-    """
-
-    async def on_push_frame(
-        self,
-        src: FrameProcessor,
-        dst: FrameProcessor,
-        frame: Frame,
-        direction: FrameDirection,
-        timestamp: int,
-    ):
-        if not isinstance(src, LLMService) and not isinstance(dst, LLMService):
-            return
-
-        time_sec = timestamp / 1_000_000_000
-
-        arrow = "→"
-
-        # Log LLM start/end frames (output)
-        if isinstance(frame, (LLMFullResponseStartFrame, LLMFullResponseEndFrame)):
-            event = "START" if isinstance(frame, LLMFullResponseStartFrame) else "END"
-            logger.debug(f"🧠 {src} {arrow} LLM {event} RESPONSE at {time_sec:.2f}s")
-        # Log all LLMTextFrames (output)
-        elif isinstance(frame, LLMTextFrame):
-            logger.debug(f"🧠 {src} {arrow} LLM GENERATING: {frame.text!r} at {time_sec:.2f}s")
-        # Log function calling (output)
-        elif (
-            isinstance(frame, FunctionCallInProgressFrame)
-            and direction != FrameDirection.DOWNSTREAM
-        ):
-            logger.debug(
-                f"🧠 {src} {arrow} LLM FUNCTION CALL ({frame.tool_call_id}): {frame.function_name!r}({frame.arguments}) at {time_sec:.2f}s"
-            )
-        # Log LLMMessagesFrame (input)
-        elif isinstance(frame, LLMMessagesFrame):
-            logger.debug(
-                f"🧠 {arrow} {dst} LLM MESSAGES FRAME: {frame.messages} at {time_sec:.2f}s"
-            )
-        # Log OpenAILLMContextFrame (input)
-        elif isinstance(frame, OpenAILLMContextFrame):
-            logger.debug(
-                f"🧠 {arrow} {dst} LLM CONTEXT FRAME: {frame.context.messages} at {time_sec:.2f}s"
-            )
-        # Log function call result (input)
-        elif isinstance(frame, FunctionCallResultFrame):
-            logger.debug(
-                f"🧠 {arrow} {src} LLM FUNCTION CALL RESULT ({frame.tool_call_id}): {frame.result} at {time_sec:.2f}s"
-            )
--- a/src/pipecat/observers/loggers/transcription_log_observer.py
+++ b/src/pipecat/observers/loggers/transcription_log_observer.py
@@ -1,54 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from loguru import logger
-
-from pipecat.frames.frames import (
-    Frame,
-    InterimTranscriptionFrame,
-    TranscriptionFrame,
-)
-from pipecat.observers.base_observer import BaseObserver
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.ai_services import STTService
-
-
-class TranscriptionLogObserver(BaseObserver):
-    """Observer to log transcription activity to the console.
-
-    Logs all frame instances (only from STT service) of:
-
-    - TranscriptionFrame
-    - InterimTranscriptionFrame
-
-    This allows you to track when the LLM starts responding, what it generates,
-    and when it finishes.
-
-    """
-
-    async def on_push_frame(
-        self,
-        src: FrameProcessor,
-        dst: FrameProcessor,
-        frame: Frame,
-        direction: FrameDirection,
-        timestamp: int,
-    ):
-        if not isinstance(src, STTService):
-            return
-
-        time_sec = timestamp / 1_000_000_000
-
-        arrow = "→"
-
-        if isinstance(frame, TranscriptionFrame):
-            logger.debug(
-                f"💬 {src} {arrow} TRANSCRIPTION: {frame.text!r} from {frame.user_id!r} at {time_sec:.2f}s"
-            )
-        elif isinstance(frame, InterimTranscriptionFrame):
-            logger.debug(
-                f"💬 {src} {arrow} INTERIM TRANSCRIPTION: {frame.text!r} from {frame.user_id!r} at {time_sec:.2f}s"
-            )
--- a/src/pipecat/processors/aggregators/llm_response.py
+++ b/src/pipecat/processors/aggregators/llm_response.py
@@ -4,16 +4,9 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

-import asyncio
-import time
-from abc import abstractmethod
-from typing import List
+from typing import List, Optional, Type

 from pipecat.frames.frames import (
-    CancelFrame,
-    EmulateUserStartedSpeakingFrame,
-    EmulateUserStoppedSpeakingFrame,
-    EndFrame,
    Frame,
    InterimTranscriptionFrame,
    LLMFullResponseEndFrame,
@@ -22,7 +15,6 @@ from pipecat.frames.frames import (
    LLMMessagesFrame,
    LLMMessagesUpdateFrame,
    LLMSetToolsFrame,
-    StartFrame,
    StartInterruptionFrame,
    TextFrame,
    TranscriptionFrame,
@@ -36,105 +28,121 @@ from pipecat.processors.aggregators.openai_llm_context import (
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor


-class BaseLLMResponseAggregator(FrameProcessor):
-    """This is the base class for all LLM response aggregators. These
-    aggregators process incoming frames and aggregate content until they are
-    ready to push the aggregation. In the case of a user, an aggregation might
-    be a full transcription received from the STT service.
-
-    The LLM response aggregators also keep a store (e.g. a message list or an
-    LLM context) of the current conversation, that is, it stores the messages
-    said by the user or by the bot.
-
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    @property
-    @abstractmethod
-    def messages(self) -> List[dict]:
-        """Returns the messages from the current conversation."""
-        pass
-
-    @property
-    @abstractmethod
-    def role(self) -> str:
-        """Returns the role (e.g. user, assistant...) for this aggregator."""
-        pass
-
-    @abstractmethod
-    def add_messages(self, messages):
-        """Add the given messages to the conversation."""
-        pass
-
-    @abstractmethod
-    def set_messages(self, messages):
-        """Reset the conversation with the given messages."""
-        pass
-
-    @abstractmethod
-    def set_tools(self, tools):
-        """Set LLM tools to be used in the current conversation."""
-        pass
-
-    @abstractmethod
-    def reset(self):
-        """Reset the internals of this aggregator. This should not modify the
-        internal messages."""
-        pass
-
-    @abstractmethod
-    async def push_aggregation(self):
-        pass
-
-
-class LLMResponseAggregator(BaseLLMResponseAggregator):
-    """This is a base LLM aggregator that uses a simple list of messages to
-    store the conversation. It pushes `LLMMessagesFrame` as an aggregation
-    frame.
-
-    """
-
+class LLMResponseAggregator(FrameProcessor):
    def __init__(
        self,
        *,
        messages: List[dict],
-        role: str = "user",
-        **kwargs,
+        role: str,
+        start_frame,
+        end_frame,
+        accumulator_frame: Type[TextFrame],
+        interim_accumulator_frame: Optional[Type[TextFrame]] = None,
+        handle_interruptions: bool = False,
+        expect_stripped_words: bool = True,  # if True, need to add spaces between words
    ):
-        super().__init__(**kwargs)
+        super().__init__()

        self._messages = messages
        self._role = role
+        self._start_frame = start_frame
+        self._end_frame = end_frame
+        self._accumulator_frame = accumulator_frame
+        self._interim_accumulator_frame = interim_accumulator_frame
+        self._handle_interruptions = handle_interruptions
+        self._expect_stripped_words = expect_stripped_words

-        self._aggregation = ""
-
-        self.reset()
+        # Reset our accumulator state.
+        self._reset()

    @property
-    def messages(self) -> List[dict]:
+    def messages(self):
        return self._messages

    @property
-    def role(self) -> str:
+    def role(self):
        return self._role

-    def add_messages(self, messages):
-        self._messages.extend(messages)
+    #
+    # Frame processor
+    #

-    def set_messages(self, messages):
-        self.reset()
-        self._messages.clear()
-        self._messages.extend(messages)
+    # Use cases implemented:
+    #
+    # S: Start, E: End, T: Transcription, I: Interim, X: Text
+    #
+    #        S E -> None
+    #      S T E -> X
+    #    S I T E -> X
+    #    S I E T -> X
+    #  S I E I T -> X
+    #      S E T -> X
+    #    S E I T -> X
+    #
+    # The following case would not be supported:
+    #
+    #    S I E T1 I T2 -> X
+    #
+    # and T2 would be dropped.

-    def set_tools(self, tools):
-        pass
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)

-    def reset(self):
-        self._aggregation = ""
+        send_aggregation = False

-    async def push_aggregation(self):
+        if isinstance(frame, self._start_frame):
+            self._aggregation = ""
+            self._aggregating = True
+            self._seen_start_frame = True
+            self._seen_end_frame = False
+            self._seen_interim_results = False
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, self._end_frame):
+            self._seen_end_frame = True
+            self._seen_start_frame = False
+
+            # We might have received the end frame but we might still be
+            # aggregating (i.e. we have seen interim results but not the final
+            # text).
+            self._aggregating = self._seen_interim_results or len(self._aggregation) == 0
+
+            # Send the aggregation if we are not aggregating anymore (i.e. no
+            # more interim results received).
+            send_aggregation = not self._aggregating
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, self._accumulator_frame):
+            if self._aggregating:
+                if self._expect_stripped_words:
+                    self._aggregation += f" {frame.text}" if self._aggregation else frame.text
+                else:
+                    self._aggregation += frame.text
+                # We have recevied a complete sentence, so if we have seen the
+                # end frame and we were still aggregating, it means we should
+                # send the aggregation.
+                send_aggregation = self._seen_end_frame
+
+            # We just got our final result, so let's reset interim results.
+            self._seen_interim_results = False
+        elif self._interim_accumulator_frame and isinstance(frame, self._interim_accumulator_frame):
+            self._seen_interim_results = True
+        elif self._handle_interruptions and isinstance(frame, StartInterruptionFrame):
+            await self._push_aggregation()
+            # Reset anyways
+            self._reset()
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, LLMMessagesAppendFrame):
+            self._add_messages(frame.messages)
+        elif isinstance(frame, LLMMessagesUpdateFrame):
+            self._set_messages(frame.messages)
+        elif isinstance(frame, LLMSetToolsFrame):
+            self._set_tools(frame.tools)
+        else:
+            await self.push_frame(frame, direction)
+
+        if send_aggregation:
+            await self._push_aggregation()
+
+    async def _push_aggregation(self):
        if len(self._aggregation) > 0:
            self._messages.append({"role": self._role, "content": self._aggregation})

@@ -145,30 +153,109 @@ class LLMResponseAggregator(BaseLLMResponseAggregator):
            frame = LLMMessagesFrame(self._messages)
            await self.push_frame(frame)

-            # Reset our accumulator state.
-            self.reset()
+    # TODO-CB: Types
+    def _add_messages(self, messages):
+        self._messages.extend(messages)
+
+    def _set_messages(self, messages):
+        self._reset()
+        self._messages.clear()
+        self._messages.extend(messages)
+
+    def _set_tools(self, tools):
+        # noop in the base class
+        pass
+
+    def _reset(self):
+        self._aggregation = ""
+        self._aggregating = False
+        self._seen_start_frame = False
+        self._seen_end_frame = False
+        self._seen_interim_results = False


-class LLMContextResponseAggregator(BaseLLMResponseAggregator):
-    """This is a base LLM aggregator that uses an LLM context to store the
-    conversation. It pushes `OpenAILLMContextFrame` as an aggregation frame.
+class LLMAssistantResponseAggregator(LLMResponseAggregator):
+    def __init__(self, messages: List[dict] = []):
+        super().__init__(
+            messages=messages,
+            role="assistant",
+            start_frame=LLMFullResponseStartFrame,
+            end_frame=LLMFullResponseEndFrame,
+            accumulator_frame=TextFrame,
+            handle_interruptions=True,
+        )

+
+class LLMUserResponseAggregator(LLMResponseAggregator):
+    def __init__(self, messages: List[dict] = []):
+        super().__init__(
+            messages=messages,
+            role="user",
+            start_frame=UserStartedSpeakingFrame,
+            end_frame=UserStoppedSpeakingFrame,
+            accumulator_frame=TranscriptionFrame,
+            interim_accumulator_frame=InterimTranscriptionFrame,
+        )
+
+
+class LLMFullResponseAggregator(FrameProcessor):
+    """This class aggregates Text frames until it receives a
+    LLMFullResponseEndFrame, then emits the concatenated text as
+    a single text frame.
+
+    given the following frames:
+
+        TextFrame("Hello,")
+        TextFrame(" world.")
+        TextFrame(" I am")
+        TextFrame(" an LLM.")
+        LLMFullResponseEndFrame()]
+
+    this processor will yield nothing for the first 4 frames, then
+
+        TextFrame("Hello, world. I am an LLM.")
+        LLMFullResponseEndFrame()
+
+    when passed the last frame.
+
+    >>> async def print_frames(aggregator, frame):
+    ...     async for frame in aggregator.process_frame(frame):
+    ...         if isinstance(frame, TextFrame):
+    ...             print(frame.text)
+    ...         else:
+    ...             print(frame.__class__.__name__)
+
+    >>> aggregator = LLMFullResponseAggregator()
+    >>> asyncio.run(print_frames(aggregator, TextFrame("Hello,")))
+    >>> asyncio.run(print_frames(aggregator, TextFrame(" world.")))
+    >>> asyncio.run(print_frames(aggregator, TextFrame(" I am")))
+    >>> asyncio.run(print_frames(aggregator, TextFrame(" an LLM.")))
+    >>> asyncio.run(print_frames(aggregator, LLMFullResponseEndFrame()))
+    Hello, world. I am an LLM.
+    LLMFullResponseEndFrame
    """

-    def __init__(self, *, context: OpenAILLMContext, role: str, **kwargs):
-        super().__init__(**kwargs)
-        self._context = context
-        self._role = role
-
+    def __init__(self):
+        super().__init__()
        self._aggregation = ""

-    @property
-    def messages(self) -> List[dict]:
-        return self._context.get_messages()
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)

-    @property
-    def role(self) -> str:
-        return self._role
+        if isinstance(frame, TextFrame):
+            self._aggregation += frame.text
+        elif isinstance(frame, LLMFullResponseEndFrame):
+            await self.push_frame(TextFrame(self._aggregation))
+            await self.push_frame(frame)
+            self._aggregation = ""
+        else:
+            await self.push_frame(frame, direction)
+
+
+class LLMContextAggregator(LLMResponseAggregator):
+    def __init__(self, *, context: OpenAILLMContext, **kwargs):
+        super().__init__(**kwargs)
+        self._context = context

    @property
    def context(self):
@@ -177,25 +264,23 @@ class LLMContextResponseAggregator(BaseLLMResponseAggregator):
    def get_context_frame(self) -> OpenAILLMContextFrame:
        return OpenAILLMContextFrame(context=self._context)

-    async def push_context_frame(self, direction: FrameDirection = FrameDirection.DOWNSTREAM):
+    async def push_context_frame(self):
        frame = self.get_context_frame()
-        await self.push_frame(frame, direction)
+        await self.push_frame(frame)

-    def add_messages(self, messages):
+    # TODO-CB: Types
+    def _add_messages(self, messages):
        self._context.add_messages(messages)

-    def set_messages(self, messages):
+    def _set_messages(self, messages):
        self._context.set_messages(messages)

-    def set_tools(self, tools: List):
+    def _set_tools(self, tools: List):
        self._context.set_tools(tools)

-    def reset(self):
-        self._aggregation = ""
-
-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if len(self._aggregation) > 0:
-            self._context.add_message({"role": self.role, "content": self._aggregation})
+            self._context.add_message({"role": self._role, "content": self._aggregation})

            # Reset the aggregation. Reset it before pushing it down, otherwise
            # if the tasks gets cancelled we won't be able to clear things up.
@@ -205,243 +290,31 @@ class LLMContextResponseAggregator(BaseLLMResponseAggregator):
            await self.push_frame(frame)

            # Reset our accumulator state.
-            self.reset()
+            self._reset()


-class LLMUserContextAggregator(LLMContextResponseAggregator):
-    """This is a user LLM aggregator that uses an LLM context to store the
-    conversation. It aggregates transcriptions from the STT service and it has
-    logic to handle multiple scenarios where transcriptions are received between
-    VAD events (`UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame`) or
-    even outside or no VAD events at all.
-
-    """
-
-    def __init__(
-        self,
-        context: OpenAILLMContext,
-        aggregation_timeout: float = 1.0,
-        bot_interruption_timeout: float = 2.0,
-        **kwargs,
-    ):
-        super().__init__(context=context, role="user", **kwargs)
-        self._aggregation_timeout = aggregation_timeout
-        self._bot_interruption_timeout = bot_interruption_timeout
-
-        self._seen_interim_results = False
-        self._user_speaking = False
-        self._last_user_speaking_time = 0
-        self._emulating_vad = False
-
-        self._aggregation_event = asyncio.Event()
-        self._aggregation_task = None
-
-        self.reset()
-
-    def reset(self):
-        super().reset()
-        self._seen_interim_results = False
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, StartFrame):
-            await self._start(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, EndFrame):
-            await self._stop(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, CancelFrame):
-            await self._cancel(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, UserStartedSpeakingFrame):
-            await self._handle_user_started_speaking(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, UserStoppedSpeakingFrame):
-            await self._handle_user_stopped_speaking(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, TranscriptionFrame):
-            await self._handle_transcription(frame)
-        elif isinstance(frame, InterimTranscriptionFrame):
-            await self._handle_interim_transcription(frame)
-        elif isinstance(frame, LLMMessagesAppendFrame):
-            self.add_messages(frame.messages)
-        elif isinstance(frame, LLMMessagesUpdateFrame):
-            self.set_messages(frame.messages)
-        elif isinstance(frame, LLMSetToolsFrame):
-            self.set_tools(frame.tools)
-        else:
-            await self.push_frame(frame, direction)
-
-    async def _start(self, frame: StartFrame):
-        self._create_aggregation_task()
-
-    async def _stop(self, frame: EndFrame):
-        await self._cancel_aggregation_task()
-
-    async def _cancel(self, frame: CancelFrame):
-        await self._cancel_aggregation_task()
-
-    async def _handle_user_started_speaking(self, _: UserStartedSpeakingFrame):
-        self._last_user_speaking_time = time.time()
-        self._user_speaking = True
-
-    async def _handle_user_stopped_speaking(self, _: UserStoppedSpeakingFrame):
-        self._last_user_speaking_time = time.time()
-        self._user_speaking = False
-        if not self._seen_interim_results:
-            await self.push_aggregation()
-
-    async def _handle_transcription(self, frame: TranscriptionFrame):
-        text = frame.text
-
-        # Make sure we really have some text.
-        if not text.strip():
-            return
-
-        self._aggregation += f" {text}" if self._aggregation else text
-        # We just got a final result, so let's reset interim results.
-        self._seen_interim_results = False
-        # Reset aggregation timer.
-        self._aggregation_event.set()
-
-    async def _handle_interim_transcription(self, _: InterimTranscriptionFrame):
-        self._seen_interim_results = True
-
-    def _create_aggregation_task(self):
-        self._aggregation_task = self.create_task(self._aggregation_task_handler())
-
-    async def _cancel_aggregation_task(self):
-        if self._aggregation_task:
-            await self.cancel_task(self._aggregation_task)
-            self._aggregation_task = None
-
-    async def _aggregation_task_handler(self):
-        while True:
-            try:
-                await asyncio.wait_for(self._aggregation_event.wait(), self._aggregation_timeout)
-                await self._maybe_push_bot_interruption()
-            except asyncio.TimeoutError:
-                if not self._user_speaking:
-                    await self.push_aggregation()
-
-                # If we are emulating VAD we still need to send the user stopped
-                # speaking frame.
-                if self._emulating_vad:
-                    await self.push_frame(
-                        EmulateUserStoppedSpeakingFrame(), FrameDirection.UPSTREAM
-                    )
-                    self._emulating_vad = False
-            finally:
-                self._aggregation_event.clear()
-
-    async def _maybe_push_bot_interruption(self):
-        """If the user stopped speaking a while back and we got a transcription
-        frame we might want to interrupt the bot.
-
-        """
-        if not self._user_speaking:
-            diff_time = time.time() - self._last_user_speaking_time
-            if diff_time > self._bot_interruption_timeout:
-                # If we reach this case we received a transcription but VAD was
-                # not able to detect voice (e.g. when you whisper a short
-                # utterance). So, we need to emulate VAD (i.e. user
-                # start/stopped speaking).
-                await self.push_frame(EmulateUserStartedSpeakingFrame(), FrameDirection.UPSTREAM)
-                self._emulating_vad = True
-
-                # Reset time so we don't interrupt again right away.
-                self._last_user_speaking_time = time.time()
+class LLMAssistantContextAggregator(LLMContextAggregator):
+    def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True):
+        super().__init__(
+            messages=[],
+            context=context,
+            role="assistant",
+            start_frame=LLMFullResponseStartFrame,
+            end_frame=LLMFullResponseEndFrame,
+            accumulator_frame=TextFrame,
+            handle_interruptions=True,
+            expect_stripped_words=expect_stripped_words,
+        )


-class LLMAssistantContextAggregator(LLMContextResponseAggregator):
-    """This is an assistant LLM aggregator that uses an LLM context to store the
-    conversation. It aggregates text frames received between
-    `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`.
-
-    """
-
-    def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True, **kwargs):
-        super().__init__(context=context, role="assistant", **kwargs)
-        self._expect_stripped_words = expect_stripped_words
-
-        self._started = False
-
-        self.reset()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, StartInterruptionFrame):
-            await self.push_aggregation()
-            # Reset anyways
-            self.reset()
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, LLMFullResponseStartFrame):
-            await self._handle_llm_start(frame)
-        elif isinstance(frame, LLMFullResponseEndFrame):
-            await self._handle_llm_end(frame)
-        elif isinstance(frame, TextFrame):
-            await self._handle_text(frame)
-        elif isinstance(frame, LLMMessagesAppendFrame):
-            self.add_messages(frame.messages)
-        elif isinstance(frame, LLMMessagesUpdateFrame):
-            self.set_messages(frame.messages)
-        elif isinstance(frame, LLMSetToolsFrame):
-            self.set_tools(frame.tools)
-        else:
-            await self.push_frame(frame, direction)
-
-    async def _handle_llm_start(self, _: LLMFullResponseStartFrame):
-        self._started = True
-
-    async def _handle_llm_end(self, _: LLMFullResponseEndFrame):
-        self._started = False
-        await self.push_aggregation()
-
-    async def _handle_text(self, frame: TextFrame):
-        if not self._started:
-            return
-
-        if self._expect_stripped_words:
-            self._aggregation += f" {frame.text}" if self._aggregation else frame.text
-        else:
-            self._aggregation += frame.text
-
-
-class LLMUserResponseAggregator(LLMUserContextAggregator):
-    def __init__(self, messages: List[dict] = [], **kwargs):
-        super().__init__(context=OpenAILLMContext(messages), **kwargs)
-
-    async def push_aggregation(self):
-        if len(self._aggregation) > 0:
-            self._context.add_message({"role": self.role, "content": self._aggregation})
-
-            # Reset the aggregation. Reset it before pushing it down, otherwise
-            # if the tasks gets cancelled we won't be able to clear things up.
-            self._aggregation = ""
-
-            frame = LLMMessagesFrame(self._context.messages)
-            await self.push_frame(frame)
-
-            # Reset our accumulator state.
-            self.reset()
-
-
-class LLMAssistantResponseAggregator(LLMAssistantContextAggregator):
-    def __init__(self, messages: List[dict] = [], **kwargs):
-        super().__init__(context=OpenAILLMContext(messages), **kwargs)
-
-    async def push_aggregation(self):
-        if len(self._aggregation) > 0:
-            self._context.add_message({"role": self.role, "content": self._aggregation})
-
-            # Reset the aggregation. Reset it before pushing it down, otherwise
-            # if the tasks gets cancelled we won't be able to clear things up.
-            self._aggregation = ""
-
-            frame = LLMMessagesFrame(self._context.messages)
-            await self.push_frame(frame)
-
-            # Reset our accumulator state.
-            self.reset()
+class LLMUserContextAggregator(LLMContextAggregator):
+    def __init__(self, context: OpenAILLMContext):
+        super().__init__(
+            messages=[],
+            context=context,
+            role="user",
+            start_frame=UserStartedSpeakingFrame,
+            end_frame=UserStoppedSpeakingFrame,
+            accumulator_frame=TranscriptionFrame,
+            interim_accumulator_frame=InterimTranscriptionFrame,
+        )
--- a/src/pipecat/processors/aggregators/user_response.py
+++ b/src/pipecat/processors/aggregators/user_response.py
@@ -4,15 +4,131 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

-from pipecat.frames.frames import TextFrame
-from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
+from typing import Optional
+
+from pipecat.frames.frames import (
+    Frame,
+    InterimTranscriptionFrame,
+    StartInterruptionFrame,
+    TextFrame,
+    TranscriptionFrame,
+    UserStartedSpeakingFrame,
+    UserStoppedSpeakingFrame,
+)
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor


-class UserResponseAggregator(LLMUserResponseAggregator):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+class ResponseAggregator(FrameProcessor):
+    """This frame processor aggregates frames between a start and an end frame
+    into complete text frame sentences.

-    async def push_aggregation(self):
+    For example, frame input/output:
+        UserStartedSpeakingFrame() -> None
+        TranscriptionFrame("Hello,") -> None
+        TranscriptionFrame(" world.") -> None
+        UserStoppedSpeakingFrame() -> TextFrame("Hello world.")
+
+    Doctest: FIXME to work with asyncio
+    >>> async def print_frames(aggregator, frame):
+    ...     async for frame in aggregator.process_frame(frame):
+    ...         if isinstance(frame, TextFrame):
+    ...             print(frame.text)
+
+    >>> aggregator = ResponseAggregator(start_frame = UserStartedSpeakingFrame,
+    ...                                 end_frame=UserStoppedSpeakingFrame,
+    ...                                 accumulator_frame=TranscriptionFrame,
+    ...                                 pass_through=False)
+    >>> asyncio.run(print_frames(aggregator, UserStartedSpeakingFrame()))
+    >>> asyncio.run(print_frames(aggregator, TranscriptionFrame("Hello,", 1, 1)))
+    >>> asyncio.run(print_frames(aggregator, TranscriptionFrame("world.",  1, 2)))
+    >>> asyncio.run(print_frames(aggregator, UserStoppedSpeakingFrame()))
+    Hello, world.
+
+    """
+
+    def __init__(
+        self,
+        *,
+        start_frame,
+        end_frame,
+        accumulator_frame: TextFrame,
+        interim_accumulator_frame: Optional[TextFrame] = None,
+    ):
+        super().__init__()
+
+        self._start_frame = start_frame
+        self._end_frame = end_frame
+        self._accumulator_frame = accumulator_frame
+        self._interim_accumulator_frame = interim_accumulator_frame
+
+        # Reset our accumulator state.
+        self._reset()
+
+    #
+    # Frame processor
+    #
+
+    # Use cases implemented:
+    #
+    # S: Start, E: End, T: Transcription, I: Interim, X: Text
+    #
+    #        S E -> None
+    #      S T E -> X
+    #    S I T E -> X
+    #    S I E T -> X
+    #  S I E I T -> X
+    #      S E T -> X
+    #    S E I T -> X
+    #
+    # The following case would not be supported:
+    #
+    #    S I E T1 I T2 -> X
+    #
+    # and T2 would be dropped.
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        send_aggregation = False
+
+        if isinstance(frame, self._start_frame):
+            self._aggregating = True
+            self._seen_start_frame = True
+            self._seen_end_frame = False
+            self._seen_interim_results = False
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, self._end_frame):
+            self._seen_end_frame = True
+            self._seen_start_frame = False
+
+            # We might have received the end frame but we might still be
+            # aggregating (i.e. we have seen interim results but not the final
+            # text).
+            self._aggregating = self._seen_interim_results or len(self._aggregation) == 0
+
+            # Send the aggregation if we are not aggregating anymore (i.e. no
+            # more interim results received).
+            send_aggregation = not self._aggregating
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, self._accumulator_frame):
+            if self._aggregating:
+                self._aggregation += f" {frame.text}"
+                # We have recevied a complete sentence, so if we have seen the
+                # end frame and we were still aggregating, it means we should
+                # send the aggregation.
+                send_aggregation = self._seen_end_frame
+
+            # We just got our final result, so let's reset interim results.
+            self._seen_interim_results = False
+        elif self._interim_accumulator_frame and isinstance(frame, self._interim_accumulator_frame):
+            self._seen_interim_results = True
+        else:
+            await self.push_frame(frame, direction)
+
+        if send_aggregation:
+            await self._push_aggregation()
+
+    async def _push_aggregation(self):
        if len(self._aggregation) > 0:
            frame = TextFrame(self._aggregation.strip())

@@ -23,4 +139,21 @@ class UserResponseAggregator(LLMUserResponseAggregator):
            await self.push_frame(frame)

            # Reset our accumulator state.
-            self.reset()
+            self._reset()
+
+    def _reset(self):
+        self._aggregation = ""
+        self._aggregating = False
+        self._seen_start_frame = False
+        self._seen_end_frame = False
+        self._seen_interim_results = False
+
+
+class UserResponseAggregator(ResponseAggregator):
+    def __init__(self):
+        super().__init__(
+            start_frame=UserStartedSpeakingFrame,
+            end_frame=UserStoppedSpeakingFrame,
+            accumulator_frame=TranscriptionFrame,
+            interim_accumulator_frame=InterimTranscriptionFrame,
+        )
--- a/src/pipecat/processors/filters/stt_mute_filter.py
+++ b/src/pipecat/processors/filters/stt_mute_filter.py
@@ -23,8 +23,6 @@ from pipecat.frames.frames import (
    Frame,
    FunctionCallInProgressFrame,
    FunctionCallResultFrame,
-    InputAudioRawFrame,
-    StartFrame,
    StartInterruptionFrame,
    StopInterruptionFrame,
    STTMuteFrame,
@@ -39,18 +37,16 @@ class STTMuteStrategy(Enum):
    """Strategies determining when STT should be muted.

    Attributes:
-        FIRST_SPEECH: Mute only during first detected bot speech
-        MUTE_UNTIL_FIRST_BOT_COMPLETE: Start muted and remain muted until first bot speech completes
+        FIRST_SPEECH: Mute only during first bot speech
        FUNCTION_CALL: Mute during function calls
        ALWAYS: Mute during all bot speech
        CUSTOM: Allow custom logic via callback
    """

-    FIRST_SPEECH = "first_speech"
-    MUTE_UNTIL_FIRST_BOT_COMPLETE = "mute_until_first_bot_complete"
-    FUNCTION_CALL = "function_call"
-    ALWAYS = "always"
-    CUSTOM = "custom"
+    FIRST_SPEECH = "first_speech"  # Mute only during first bot speech
+    FUNCTION_CALL = "function_call"  # Mute during function calls
+    ALWAYS = "always"  # Mute during all bot speech
+    CUSTOM = "custom"  # Allow custom logic via callback


@dataclass
@@ -61,25 +57,12 @@ class STTMuteConfig:
        strategies: Set of muting strategies to apply
        should_mute_callback: Optional callback for custom muting logic.
            Only required when using STTMuteStrategy.CUSTOM
-
-    Note:
-        MUTE_UNTIL_FIRST_BOT_COMPLETE and FIRST_SPEECH strategies should not be used together
-        as they handle the first bot speech differently.
    """

    strategies: set[STTMuteStrategy]
    # Optional callback for custom muting logic
    should_mute_callback: Optional[Callable[["STTMuteFilter"], Awaitable[bool]]] = None

-    def __post_init__(self):
-        if (
-            STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE in self.strategies
-            and STTMuteStrategy.FIRST_SPEECH in self.strategies
-        ):
-            raise ValueError(
-                "MUTE_UNTIL_FIRST_BOT_COMPLETE and FIRST_SPEECH strategies should not be used together"
-            )
-

 class STTMuteFilter(FrameProcessor):
    """A processor that handles STT muting and interruption control.
@@ -110,7 +93,7 @@ class STTMuteFilter(FrameProcessor):
        self._first_speech_handled = False
        self._bot_is_speaking = False
        self._function_call_in_progress = False
-        self._is_muted = False  # Initialize as unmuted, will set state on StartFrame if needed
+        self._is_muted = False

    @property
    def is_muted(self) -> bool:
@@ -141,10 +124,6 @@ class STTMuteFilter(FrameProcessor):
                        self._first_speech_handled = True
                        return True

-                case STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE:
-                    if not self._first_speech_handled:
-                        return True
-
                case STTMuteStrategy.CUSTOM:
                    if self._bot_is_speaking and self._config.should_mute_callback:
                        should_mute = await self._config.should_mute_callback(self)
@@ -154,31 +133,25 @@ class STTMuteFilter(FrameProcessor):
        return False

    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Processes incoming frames and manages muting state."""
        await super().process_frame(frame, direction)

-        # Determine if we need to change mute state based on frame type
-        should_mute = None
-
-        # Process frames to determine mute state
-        if isinstance(frame, StartFrame):
-            should_mute = await self._should_mute()
-        elif isinstance(frame, FunctionCallInProgressFrame):
+        """Processes incoming frames and manages muting state."""
+        # Handle function call state changes
+        if isinstance(frame, FunctionCallInProgressFrame):
            self._function_call_in_progress = True
-            should_mute = await self._should_mute()
+            await self._handle_mute_state(await self._should_mute())
        elif isinstance(frame, FunctionCallResultFrame):
            self._function_call_in_progress = False
-            should_mute = await self._should_mute()
+            await self._handle_mute_state(await self._should_mute())
+        # Handle bot speaking state changes
        elif isinstance(frame, BotStartedSpeakingFrame):
            self._bot_is_speaking = True
-            should_mute = await self._should_mute()
+            await self._handle_mute_state(await self._should_mute())
        elif isinstance(frame, BotStoppedSpeakingFrame):
            self._bot_is_speaking = False
-            if not self._first_speech_handled:
-                self._first_speech_handled = True
-            should_mute = await self._should_mute()
+            await self._handle_mute_state(await self._should_mute())

-        # Then push the original frame
+        # Handle frame propagation
        if isinstance(
            frame,
            (
@@ -186,18 +159,13 @@ class STTMuteFilter(FrameProcessor):
                StopInterruptionFrame,
                UserStartedSpeakingFrame,
                UserStoppedSpeakingFrame,
-                InputAudioRawFrame,
            ),
        ):
            # Only pass VAD-related frames when not muted
            if not self.is_muted:
                await self.push_frame(frame, direction)
            else:
-                logger.trace(f"{frame.__class__.__name__} suppressed - STT currently muted")
+                logger.debug(f"{frame.__class__.__name__} suppressed - STT currently muted")
        else:
            # Pass all other frames through
            await self.push_frame(frame, direction)
-
-        # Finally handle mute state change if needed
-        if should_mute is not None and should_mute != self.is_muted:
-            await self._handle_mute_state(should_mute)
--- a/src/pipecat/processors/frame_processor.py
+++ b/src/pipecat/processors/frame_processor.py
@@ -73,11 +73,10 @@ class FrameProcessor:
        self._metrics.set_processor_name(self.name)

        # Processors have an input queue. The input queue will be processed
-        # immediately (default) or it will block if `pause_processing_frames()`
-        # is called. To resume processing frames we need to call
-        # `resume_processing_frames()` which will wake up the event.
+        # immediately (default) or it will block if `pause_processing_frames()` is
+        # called. To resume processing frames we need to call
+        # `resume_processing_frames()`.
        self.__should_block_frames = False
-        self.__input_event = asyncio.Event()
        self.__input_frame_task: Optional[asyncio.Task] = None

        # Every processor in Pipecat should only output frames from a single
@@ -336,8 +335,8 @@ class FrameProcessor:
    def __create_input_task(self):
        if not self.__input_frame_task:
            self.__should_block_frames = False
-            self.__input_event.clear()
            self.__input_queue = asyncio.Queue()
+            self.__input_event = asyncio.Event()
            self.__input_frame_task = self.create_task(self.__input_frame_task_handler())

    async def __cancel_input_task(self):
--- a/src/pipecat/processors/frameworks/rtvi.py
+++ b/src/pipecat/processors/frameworks/rtvi.py
@@ -5,7 +5,6 @@
 #

 import asyncio
-import base64
 from dataclasses import dataclass
 from typing import (
    Any,
@@ -32,7 +31,6 @@ from pipecat.frames.frames import (
    ErrorFrame,
    Frame,
    FunctionCallResultFrame,
-    InputAudioRawFrame,
    InterimTranscriptionFrame,
    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
@@ -60,9 +58,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContextFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.transports.base_input import BaseInputTransport
 from pipecat.transports.base_output import BaseOutputTransport
-from pipecat.transports.base_transport import BaseTransport
 from pipecat.utils.string import match_endofsentence

 RTVI_PROTOCOL_VERSION = "0.3.0"
@@ -823,7 +819,6 @@ class RTVIProcessor(FrameProcessor):
        self,
        *,
        config: RTVIConfig = RTVIConfig(config=[]),
-        transport: Optional[BaseTransport] = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
@@ -849,14 +844,6 @@ class RTVIProcessor(FrameProcessor):
        self._register_event_handler("on_bot_started")
        self._register_event_handler("on_client_ready")

-        self._input_transport = None
-        self._transport = transport
-        if self._transport:
-            input_transport = self._transport.input()
-            if isinstance(input_transport, BaseInputTransport):
-                self._input_transport = input_transport
-                self._input_transport.enable_audio_in_stream_on_start(False)
-
    def observer(self) -> RTVIObserver:
        import warnings

@@ -1026,8 +1013,6 @@ class RTVIProcessor(FrameProcessor):
                case "llm-function-call-result":
                    data = RTVILLMFunctionCallResultData.model_validate(message.data)
                    await self._handle_function_call_result(data)
-                case "raw-audio" | "raw-audio-batch":
-                    await self._handle_audio_buffer(message.data)

                case _:
                    await self._send_error_response(message.id, f"Unsupported type {message.type}")
@@ -1040,34 +1025,9 @@ class RTVIProcessor(FrameProcessor):
            logger.warning(f"Exception processing message: {e}")

    async def _handle_client_ready(self, request_id: str):
-        logger.debug("Received client-ready")
-        if self._input_transport:
-            self._input_transport.start_audio_in_streaming()
-
        self._client_ready_id = request_id
        await self.set_client_ready()

-    async def _handle_audio_buffer(self, data):
-        if not self._input_transport:
-            return
-
-        # Extract audio batch ensuring it's a list
-        audio_list = data.get("base64AudioBatch") or [data.get("base64Audio")]
-
-        try:
-            for base64_audio in filter(None, audio_list):  # Filter out None values
-                pcm_bytes = base64.b64decode(base64_audio)
-                frame = InputAudioRawFrame(
-                    audio=pcm_bytes,
-                    sample_rate=data["sampleRate"],
-                    num_channels=data["numChannels"],
-                )
-                await self._input_transport.push_audio_frame(frame)
-
-        except (KeyError, TypeError, ValueError) as e:
-            # Handle missing keys, decoding errors, and invalid types
-            logger.error(f"Error processing audio buffer: {e}")
-
    async def _handle_describe_config(self, request_id: str):
        services = list(self._registered_services.values())
        message = RTVIDescribeConfig(id=request_id, data=RTVIDescribeConfigData(config=services))
--- a/src/pipecat/services/ai_services.py
+++ b/src/pipecat/services/ai_services.py
@@ -15,7 +15,6 @@ from loguru import logger
 from pipecat.audio.utils import calculate_audio_volume, exp_smoothing
 from pipecat.frames.frames import (
    AudioRawFrame,
-    BotStoppedSpeakingFrame,
    CancelFrame,
    EndFrame,
    ErrorFrame,
@@ -76,13 +75,13 @@ class AIService(FrameProcessor):
        )

        for key, value in settings.items():
-            logger.debug("Update request for:", key, value)
+            print("Update request for:", key, value)

            if key in self._settings:
                logger.info(f"Updating LLM setting {key} to: [{value}]")
                self._settings[key] = value
            elif key in SessionProperties.model_fields:
-                logger.debug("Attempting to update", key, value)
+                print("Attempting to update", key, value)

                try:
                    from pipecat.services.openai_realtime_beta.events import (
@@ -175,7 +174,6 @@ class LLMService(AIService):
            f = self._callbacks[None]
        else:
            return None
-        await self.call_start_function(context, function_name)
        await context.call_function(
            f,
            function_name=function_name,
@@ -209,13 +207,11 @@ class TTSService(AIService):
        # if True, TTSService will push TTSStoppedFrames, otherwise subclass must do it
        push_stop_frames: bool = False,
        # if push_stop_frames is True, wait for this idle period before pushing TTSStoppedFrame
-        stop_frame_timeout_s: float = 2.0,
+        stop_frame_timeout_s: float = 1.0,
        # if True, TTSService will push silence audio frames after TTSStoppedFrame
        push_silence_after_stop: bool = False,
        # if push_silence_after_stop is True, send this amount of audio silence
        silence_time_s: float = 2.0,
-        # if True, we will pause processing frames while we are receiving audio
-        pause_frame_processing: bool = False,
        # TTS output sample rate
        sample_rate: Optional[int] = None,
        text_filter: Optional[BaseTextFilter] = None,
@@ -228,7 +224,6 @@ class TTSService(AIService):
        self._stop_frame_timeout_s: float = stop_frame_timeout_s
        self._push_silence_after_stop: bool = push_silence_after_stop
        self._silence_time_s: float = silence_time_s
-        self._pause_frame_processing: bool = pause_frame_processing
        self._init_sample_rate = sample_rate
        self._sample_rate = 0
        self._voice_id: str = ""
@@ -239,7 +234,6 @@ class TTSService(AIService):
        self._stop_frame_queue: asyncio.Queue = asyncio.Queue()

        self._current_sentence: str = ""
-        self._processing_text: bool = False

    @property
    def sample_rate(self) -> int:
@@ -305,7 +299,6 @@ class TTSService(AIService):

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
-
        if (
            isinstance(frame, TextFrame)
            and not isinstance(frame, InterimTranscriptionFrame)
@@ -314,16 +307,9 @@ class TTSService(AIService):
            await self._process_text_frame(frame)
        elif isinstance(frame, StartInterruptionFrame):
            await self._handle_interruption(frame, direction)
-            await self.push_frame(frame, direction)
        elif isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
-            # We pause processing incoming frames if the LLM response included
-            # text (it might be that it's only a function calling response). We
-            # pause to avoid audio overlapping.
-            await self._maybe_pause_frame_processing()
-
            sentence = self._current_sentence
            self._current_sentence = ""
-            self._processing_text = False
            await self._push_tts_frames(sentence)
            if isinstance(frame, LLMFullResponseEndFrame):
                if self._push_text_frames:
@@ -332,16 +318,9 @@ class TTSService(AIService):
                await self.push_frame(frame, direction)
        elif isinstance(frame, TTSSpeakFrame):
            await self._push_tts_frames(frame.text)
-            # We pause processing incoming frames because we are sending data to
-            # the TTS. We pause to avoid audio overlapping.
-            await self._maybe_pause_frame_processing()
            await self.flush_audio()
-            self._processing_text = False
        elif isinstance(frame, TTSUpdateSettingsFrame):
            await self._update_settings(frame.settings)
-        elif isinstance(frame, BotStoppedSpeakingFrame):
-            await self._maybe_resume_frame_processing()
-            await self.push_frame(frame, direction)
        else:
            await self.push_frame(frame, direction)

@@ -368,17 +347,9 @@ class TTSService(AIService):

    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
        self._current_sentence = ""
-        self._processing_text = False
        if self._text_filter:
            self._text_filter.handle_interruption()
-
-    async def _maybe_pause_frame_processing(self):
-        if self._processing_text and self._pause_frame_processing:
-            await self.pause_processing_frames()
-
-    async def _maybe_resume_frame_processing(self):
-        if self._pause_frame_processing:
-            await self.resume_processing_frames()
+        await self.push_frame(frame, direction)

    async def _process_text_frame(self, frame: TextFrame):
        text: Optional[str] = None
@@ -400,11 +371,6 @@ class TTSService(AIService):
        if not text.strip():
            return

-        # This is just a flag that indicates if we sent something to the TTS
-        # service. It will be cleared if we sent text because of a TTSSpeakFrame
-        # or when we received an LLMFullResponseEndFrame
-        self._processing_text = True
-
        await self.start_processing_metrics()
        if self._text_filter:
            self._text_filter.reset_interruption()
@@ -453,7 +419,7 @@ class WordTTSService(TTSService):

    async def start(self, frame: StartFrame):
        await super().start(frame)
-        self._create_words_task()
+        await self._create_words_task()

    async def stop(self, frame: EndFrame):
        await super().stop(frame)
@@ -473,7 +439,7 @@ class WordTTSService(TTSService):
        await super()._handle_interruption(frame, direction)
        self.reset_word_timestamps()

-    def _create_words_task(self):
+    async def _create_words_task(self):
        self._words_task = self.create_task(self._words_task_handler())

    async def _stop_words_task(self):
@@ -503,115 +469,6 @@ class WordTTSService(TTSService):
            self._words_queue.task_done()


-class AudioContextWordTTSService(WordTTSService):
-    """This services allow us to send multiple TTS request to the services. Each
-    request could be multiple sentences long which are grouped by context. For
-    this to work, the TTS service needs to support handling multiple requests at
-    once (i.e. multiple simultaneous contexts).
-
-    The audio received from the TTS will be played in context order. That is, if
-    we requested audio for a context "A" and then audio for context "B", the
-    audio from context ID "A" will be played first.
-
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._contexts_queue = asyncio.Queue()
-        self._contexts: Dict[str, asyncio.Queue] = {}
-        self._audio_context_task = None
-
-    async def create_audio_context(self, context_id: str):
-        """Create a new audio context."""
-        await self._contexts_queue.put(context_id)
-        self._contexts[context_id] = asyncio.Queue()
-        logger.trace(f"{self} created audio context {context_id}")
-
-    async def append_to_audio_context(self, context_id: str, frame: TTSAudioRawFrame):
-        """Append audio to an existing context."""
-        if self.audio_context_available(context_id):
-            logger.trace(f"{self} appending audio {frame} to audio context {context_id}")
-            await self._contexts[context_id].put(frame)
-        else:
-            logger.warning(f"{self} unable to append audio to context {context_id}")
-
-    async def remove_audio_context(self, context_id: str):
-        """Remove an existing audio context."""
-        if self.audio_context_available(context_id):
-            # We just mark the audio context for deletion by appending
-            # None. Once we reach None while handling audio we know we can
-            # safely remove the context.
-            logger.trace(f"{self} marking audio context {context_id} for deletion")
-            await self._contexts[context_id].put(None)
-        else:
-            logger.warning(f"{self} unable to remove context {context_id}")
-
-    def audio_context_available(self, context_id: str) -> bool:
-        """Checks whether the given audio context is registered."""
-        return context_id in self._contexts
-
-    async def start(self, frame: StartFrame):
-        await super().start(frame)
-        self._create_audio_context_task()
-
-    async def stop(self, frame: EndFrame):
-        await super().stop(frame)
-        await self._stop_audio_context_task()
-
-    async def cancel(self, frame: CancelFrame):
-        await super().cancel(frame)
-        await self._stop_audio_context_task()
-
-    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
-        await super()._handle_interruption(frame, direction)
-        await self._stop_audio_context_task()
-        self._create_audio_context_task()
-
-    def _create_audio_context_task(self):
-        self._contexts_queue = asyncio.Queue()
-        self._contexts: Dict[str, asyncio.Queue] = {}
-        self._audio_context_task = self.create_task(self._audio_context_task_handler())
-
-    async def _stop_audio_context_task(self):
-        if self._audio_context_task:
-            await self.cancel_task(self._audio_context_task)
-            self._audio_context_task = None
-
-    async def _audio_context_task_handler(self):
-        """In this task we process audio contexts in order."""
-        while True:
-            context_id = await self._contexts_queue.get()
-
-            # Process the audio context until the context doesn't have more
-            # audio available (i.e. we find None).
-            await self._handle_audio_context(context_id)
-
-            # We just finished processing the context, so we can safely remove it.
-            del self._contexts[context_id]
-            self._contexts_queue.task_done()
-
-            # Append some silence between sentences.
-            silence = b"\x00" * self.sample_rate
-            frame = TTSAudioRawFrame(audio=silence, sample_rate=self.sample_rate, num_channels=1)
-            await self.push_frame(frame)
-
-    async def _handle_audio_context(self, context_id: str):
-        # If we don't receive any audio during this time, we consider the context finished.
-        AUDIO_CONTEXT_TIMEOUT = 3.0
-        queue = self._contexts[context_id]
-        running = True
-        while running:
-            try:
-                frame = await asyncio.wait_for(queue.get(), timeout=AUDIO_CONTEXT_TIMEOUT)
-                if frame:
-                    await self.push_frame(frame)
-                running = frame is not None
-            except asyncio.TimeoutError:
-                # We didn't get audio, so let's consider this context finished.
-                logger.trace(f"{self} time out on audio context {context_id}")
-                break
-
-
 class STTService(AIService):
    """STTService is a base class for speech-to-text services."""

@@ -668,13 +525,9 @@ class STTService(AIService):
            else:
                logger.warning(f"Unknown setting for STT service: {key}")

-    async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection):
-        if self._muted:
-            return
-
-        await self.process_generator(self.run_stt(frame.audio))
-        if self._audio_passthrough:
-            await self.push_frame(frame, direction)
+    async def process_audio_frame(self, frame: AudioRawFrame):
+        if not self._muted:
+            await self.process_generator(self.run_stt(frame.audio))

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        """Processes a frame of audio data, either buffering or transcribing it."""
@@ -684,7 +537,9 @@ class STTService(AIService):
            # In this service we accumulate audio internally and at the end we
            # push a TextFrame. We also push audio downstream in case someone
            # else needs it.
-            await self.process_audio_frame(frame, direction)
+            await self.process_audio_frame(frame)
+            if self._audio_passthrough:
+                await self.push_frame(frame, direction)
        elif isinstance(frame, STTUpdateSettingsFrame):
            await self._update_settings(frame.settings)
        elif isinstance(frame, STTMuteFrame):
@@ -720,7 +575,7 @@ class SegmentedSTTService(STTService):
        self._smoothing_factor = 0.2
        self._prev_volume = 0

-    async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection):
+    async def process_audio_frame(self, frame: AudioRawFrame):
        # Try to filter out empty background noise
        volume = self._get_smoothed_volume(frame)
        if volume >= self._min_volume:
--- a/src/pipecat/services/anthropic.py
+++ b/src/pipecat/services/anthropic.py
@@ -126,11 +126,9 @@ class AnthropicLLMService(LLMService):
    def create_context_aggregator(
        context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
    ) -> AnthropicContextAggregatorPair:
-        if isinstance(context, OpenAILLMContext):
-            context = AnthropicLLMContext.from_openai_context(context)
        user = AnthropicUserContextAggregator(context)
        assistant = AnthropicAssistantContextAggregator(
-            context, expect_stripped_words=assistant_expect_stripped_words
+            user, expect_stripped_words=assistant_expect_stripped_words
        )
        return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)

@@ -653,8 +651,11 @@ class AnthropicLLMContext(OpenAILLMContext):


 class AnthropicUserContextAggregator(LLMUserContextAggregator):
-    def __init__(self, context: OpenAILLMContext | AnthropicLLMContext, **kwargs):
-        super().__init__(context=context, **kwargs)
+    def __init__(self, context: OpenAILLMContext | AnthropicLLMContext):
+        super().__init__(context=context)
+
+        if isinstance(context, OpenAILLMContext):
+            self._context = AnthropicLLMContext.from_openai_context(context)

    async def process_frame(self, frame, direction):
        await super().process_frame(frame, direction)
@@ -702,8 +703,9 @@ class AnthropicUserContextAggregator(LLMUserContextAggregator):


 class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
-    def __init__(self, context: OpenAILLMContext | AnthropicLLMContext, **kwargs):
-        super().__init__(context=context, **kwargs)
+    def __init__(self, user_context_aggregator: AnthropicUserContextAggregator, **kwargs):
+        super().__init__(context=user_context_aggregator._context, **kwargs)
+        self._user_context_aggregator = user_context_aggregator
        self._function_call_in_progress = None
        self._function_call_result = None
        self._pending_image_frame_message = None
@@ -723,7 +725,7 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
            ):
                self._function_call_in_progress = None
                self._function_call_result = frame
-                await self.push_aggregation()
+                await self._push_aggregation()
            else:
                logger.warning(
                    "FunctionCallResultFrame tool_call_id != InProgressFrame tool_call_id"
@@ -732,9 +734,9 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
                self._function_call_result = None
        elif isinstance(frame, AnthropicImageMessageFrame):
            self._pending_image_frame_message = frame
-            await self.push_aggregation()
+            await self._push_aggregation()

-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if not (
            self._aggregation or self._function_call_result or self._pending_image_frame_message
        ):
@@ -743,19 +745,18 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
        run_llm = False
        properties: Optional[FunctionCallResultProperties] = None

-        aggregation = self._aggregation.strip()
-        self.reset()
+        aggregation = self._aggregation
+        self._reset()

        try:
-            if aggregation:
-                self._context.add_message({"role": "assistant", "content": aggregation})
-
            if self._function_call_result:
                frame = self._function_call_result
                properties = frame.properties
                self._function_call_result = None
                if frame.result:
                    assistant_message = {"role": "assistant", "content": []}
+                    if aggregation:
+                        assistant_message["content"].append({"type": "text", "text": aggregation})
                    assistant_message["content"].append(
                        {
                            "type": "tool_use",
@@ -783,6 +784,8 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
                    else:
                        # Default behavior
                        run_llm = True
+            elif aggregation:
+                self._context.add_message({"role": "assistant", "content": aggregation})

            if self._pending_image_frame_message:
                frame = self._pending_image_frame_message
@@ -796,14 +799,15 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
                run_llm = True

            if run_llm:
-                await self.push_context_frame(FrameDirection.UPSTREAM)
+                await self._user_context_aggregator.push_context_frame()

            # Emit the on_context_updated callback once the function call result is added to the context
            if properties and properties.on_context_updated is not None:
                await properties.on_context_updated()

            # Push context frame
-            await self.push_context_frame()
+            frame = OpenAILLMContextFrame(self._context)
+            await self.push_frame(frame)

            # Push timestamp frame with current time
            timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601())
--- a/src/pipecat/services/base_whisper.py
+++ b/src/pipecat/services/base_whisper.py
@@ -111,8 +111,6 @@ class BaseWhisperSTTService(SegmentedSTTService):
        api_key: Service API key. Defaults to None.
        base_url: Service API base URL. Defaults to None.
        language: Language of the audio input. Defaults to English.
-        prompt: Optional text to guide the model's style or continue a previous segment.
-        temperature: Sampling temperature between 0 and 1. Defaults to 0.0.
        **kwargs: Additional arguments passed to SegmentedSTTService.
    """

@@ -123,16 +121,12 @@ class BaseWhisperSTTService(SegmentedSTTService):
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        language: Optional[Language] = Language.EN,
-        prompt: Optional[str] = None,
-        temperature: Optional[float] = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.set_model_name(model)
        self._client = self._create_client(api_key, base_url)
        self._language = self.language_to_service_language(language or Language.EN)
-        self._prompt = prompt
-        self._temperature = temperature

    def _create_client(self, api_key: Optional[str], base_url: Optional[str]):
        return AsyncOpenAI(api_key=api_key, base_url=base_url)
--- a/src/pipecat/services/cartesia.py
+++ b/src/pipecat/services/cartesia.py
@@ -27,7 +27,7 @@ from pipecat.frames.frames import (
    TTSStoppedFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AudioContextWordTTSService, TTSService
+from pipecat.services.ai_services import TTSService, WordTTSService
 from pipecat.services.websocket_service import WebsocketService
 from pipecat.transcriptions.language import Language

@@ -75,7 +75,7 @@ def language_to_cartesia_language(language: Language) -> Optional[str]:
    return result


-class CartesiaTTSService(AudioContextWordTTSService, WebsocketService):
+class CartesiaTTSService(WordTTSService, WebsocketService):
    class InputParams(BaseModel):
        language: Optional[Language] = Language.EN
        speed: Optional[Union[str, float]] = ""
@@ -105,11 +105,10 @@ class CartesiaTTSService(AudioContextWordTTSService, WebsocketService):
        # if we're interrupted. Cartesia gives us word-by-word timestamps. We
        # can use those to generate text frames ourselves aligned with the
        # playout timing of the audio!
-        AudioContextWordTTSService.__init__(
+        WordTTSService.__init__(
            self,
            aggregate_sentences=True,
            push_text_frames=False,
-            pause_frame_processing=True,
            sample_rate=sample_rate,
            **kwargs,
        )
@@ -192,12 +191,12 @@ class CartesiaTTSService(AudioContextWordTTSService, WebsocketService):
        self._receive_task = self.create_task(self._receive_task_handler(self.push_error))

    async def _disconnect(self):
+        await self._disconnect_websocket()
+
        if self._receive_task:
            await self.cancel_task(self._receive_task)
            self._receive_task = None

-        await self._disconnect_websocket()
-
    async def _connect_websocket(self):
        try:
            logger.debug("Connecting to Cartesia")
@@ -245,14 +244,17 @@ class CartesiaTTSService(AudioContextWordTTSService, WebsocketService):
    async def _receive_messages(self):
        async for message in self._get_websocket():
            msg = json.loads(message)
-            if not msg or not self.audio_context_available(msg["context_id"]):
+            if not msg or msg["context_id"] != self._context_id:
                continue
            if msg["type"] == "done":
                await self.stop_ttfb_metrics()
+                # Unset _context_id but not the _context_id_start_timestamp
+                # because we are likely still playing out audio and need the
+                # timestamp to set send context frames.
+                self._context_id = None
                await self.add_word_timestamps(
                    [("TTSStoppedFrame", 0), ("LLMFullResponseEndFrame", 0), ("Reset", 0)]
                )
-                await self.remove_audio_context(msg["context_id"])
            elif msg["type"] == "timestamps":
                await self.add_word_timestamps(
                    list(zip(msg["word_timestamps"]["words"], msg["word_timestamps"]["start"]))
@@ -265,16 +267,28 @@ class CartesiaTTSService(AudioContextWordTTSService, WebsocketService):
                    sample_rate=self.sample_rate,
                    num_channels=1,
                )
-                await self.append_to_audio_context(msg["context_id"], frame)
+                await self.push_frame(frame)
            elif msg["type"] == "error":
                logger.error(f"{self} error: {msg}")
                await self.push_frame(TTSStoppedFrame())
                await self.stop_all_metrics()
                await self.push_error(ErrorFrame(f"{self} error: {msg['error']}"))
-                self._context_id = None
            else:
                logger.error(f"{self} error, unknown message type: {msg}")

+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        # If we received a TTSSpeakFrame and the LLM response included text (it
+        # might be that it's only a function calling response) we pause
+        # processing more frames until we receive a BotStoppedSpeakingFrame.
+        if isinstance(frame, TTSSpeakFrame):
+            await self.pause_processing_frames()
+        elif isinstance(frame, LLMFullResponseEndFrame) and self._context_id:
+            await self.pause_processing_frames()
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.resume_processing_frames()
+
    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
        logger.debug(f"Generating TTS: [{text}]")

@@ -286,7 +300,6 @@ class CartesiaTTSService(AudioContextWordTTSService, WebsocketService):
                await self.start_ttfb_metrics()
                yield TTSStartedFrame()
                self._context_id = str(uuid.uuid4())
-                await self.create_audio_context(self._context_id)

            msg = self._build_msg(text=text or " ")  # Text must contain at least one character

@@ -364,6 +377,9 @@ class CartesiaHttpTTSService(TTSService):
    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
        logger.debug(f"Generating TTS: [{text}]")

+        await self.start_ttfb_metrics()
+        yield TTSStartedFrame()
+
        try:
            voice_controls = None
            if self._settings["speed"] or self._settings["emotion"]:
@@ -373,8 +389,6 @@ class CartesiaHttpTTSService(TTSService):
                if self._settings["emotion"]:
                    voice_controls["emotion"] = self._settings["emotion"]

-            await self.start_ttfb_metrics()
-
            output = await self._client.tts.sse(
                model_id=self._model_name,
                transcript=text,
@@ -385,17 +399,14 @@ class CartesiaHttpTTSService(TTSService):
                _experimental_voice_controls=voice_controls,
            )

-            await self.start_tts_usage_metrics(text)
-
-            yield TTSStartedFrame()
-
            frame = TTSAudioRawFrame(
                audio=output["audio"], sample_rate=self.sample_rate, num_channels=1
            )
-
            yield frame
        except Exception as e:
            logger.error(f"{self} exception: {e}")
-        finally:
-            await self.stop_ttfb_metrics()
-            yield TTSStoppedFrame()
+
+        await self.start_tts_usage_metrics(text)
+
+        await self.stop_ttfb_metrics()
+        yield TTSStoppedFrame()
--- a/src/pipecat/services/cerebras.py
+++ b/src/pipecat/services/cerebras.py
@@ -7,7 +7,6 @@
 from typing import List

 from loguru import logger
-from tenacity import retry, stop_after_attempt, wait_fixed

 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
@@ -53,7 +52,6 @@ class CerebrasLLMService(OpenAILLMService):
        logger.debug(f"Creating Cerebras client with api {base_url}")
        return super().create_client(api_key, base_url, **kwargs)

-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(2))
    async def get_chat_completions(
        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
    ) -> AsyncStream[ChatCompletionChunk]:
--- a/src/pipecat/services/deepgram.py
+++ b/src/pipecat/services/deepgram.py
@@ -5,7 +5,7 @@
 #

 import asyncio
-from typing import AsyncGenerator, Dict, Optional
+from typing import AsyncGenerator, Optional

 from loguru import logger

@@ -34,7 +34,6 @@ try:
        AsyncListenWebSocketClient,
        DeepgramClient,
        DeepgramClientOptions,
-        ErrorResponse,
        LiveOptions,
        LiveResultResponse,
        LiveTranscriptionEvents,
@@ -121,16 +120,14 @@ class DeepgramSTTService(STTService):
        url: str = "",
        sample_rate: Optional[int] = None,
        live_options: Optional[LiveOptions] = None,
-        addons: Optional[Dict] = None,
        **kwargs,
    ):
-        sample_rate = sample_rate or (live_options.sample_rate if live_options else None)
        super().__init__(sample_rate=sample_rate, **kwargs)

        default_options = LiveOptions(
            encoding="linear16",
            language=Language.EN,
-            model="nova-3-general",
+            model="nova-2-general",
            channels=1,
            interim_results=True,
            smart_format=True,
@@ -150,7 +147,6 @@ class DeepgramSTTService(STTService):
            merged_options.language = merged_options.language.value

        self._settings = merged_options.to_dict()
-        self._addons = addons

        self._client = DeepgramClient(
            api_key,
@@ -159,10 +155,13 @@ class DeepgramSTTService(STTService):
                options={"keepalive": "true"},  # verbose=logging.DEBUG
            ),
        )
-
+        self._connection: AsyncListenWebSocketClient = self._client.listen.asyncwebsocket.v("1")
+        self._connection.on(LiveTranscriptionEvents.Transcript, self._on_message)
        if self.vad_enabled:
            self._register_event_handler("on_speech_started")
            self._register_event_handler("on_utterance_end")
+            self._connection.on(LiveTranscriptionEvents.SpeechStarted, self._on_speech_started)
+            self._connection.on(LiveTranscriptionEvents.UtteranceEnd, self._on_utterance_end)

    @property
    def vad_enabled(self):
@@ -203,25 +202,7 @@ class DeepgramSTTService(STTService):

    async def _connect(self):
        logger.debug("Connecting to Deepgram")
-
-        self._connection: AsyncListenWebSocketClient = self._client.listen.asyncwebsocket.v("1")
-
-        self._connection.on(
-            LiveTranscriptionEvents(LiveTranscriptionEvents.Transcript), self._on_message
-        )
-        self._connection.on(LiveTranscriptionEvents(LiveTranscriptionEvents.Error), self._on_error)
-
-        if self.vad_enabled:
-            self._connection.on(
-                LiveTranscriptionEvents(LiveTranscriptionEvents.SpeechStarted),
-                self._on_speech_started,
-            )
-            self._connection.on(
-                LiveTranscriptionEvents(LiveTranscriptionEvents.UtteranceEnd),
-                self._on_utterance_end,
-            )
-
-        if not await self._connection.start(options=self._settings, addons=self._addons):
+        if not await self._connection.start(self._settings):
            logger.error(f"{self}: unable to connect to Deepgram")

    async def _disconnect(self):
@@ -233,15 +214,6 @@ class DeepgramSTTService(STTService):
        await self.start_ttfb_metrics()
        await self.start_processing_metrics()

-    async def _on_error(self, *args, **kwargs):
-        error: ErrorResponse = kwargs["error"]
-        logger.warning(f"{self} connection error, will retry: {error}")
-        await self.stop_all_metrics()
-        # NOTE(aleix): we don't disconnect (i.e. call finish on the connection)
-        # because this triggers more errors internally in the Deepgram SDK. So,
-        # we just forget about the previous connection and create a new one.
-        await self._connect()
-
    async def _on_speech_started(self, *args, **kwargs):
        await self.start_metrics()
        await self._call_event_handler("on_speech_started", *args, **kwargs)
--- a/src/pipecat/services/deepseek.py
+++ b/src/pipecat/services/deepseek.py
@@ -8,7 +8,6 @@
 from typing import List

 from loguru import logger
-from tenacity import retry, stop_after_attempt, wait_fixed

 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
@@ -54,7 +53,6 @@ class DeepSeekLLMService(OpenAILLMService):
        logger.debug(f"Creating DeepSeek client with api {base_url}")
        return super().create_client(api_key, base_url, **kwargs)

-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(2))
    async def get_chat_completions(
        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
    ) -> AsyncStream[ChatCompletionChunk]:
--- a/src/pipecat/services/elevenlabs.py
+++ b/src/pipecat/services/elevenlabs.py
@@ -191,7 +191,7 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService):
            aggregate_sentences=True,
            push_text_frames=False,
            push_stop_frames=True,
-            pause_frame_processing=True,
+            stop_frame_timeout_s=2.0,
            sample_rate=sample_rate,
            **kwargs,
        )
@@ -289,6 +289,19 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService):
            if isinstance(frame, TTSStoppedFrame):
                await self.add_word_timestamps([("LLMFullResponseEndFrame", 0), ("Reset", 0)])

+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        # If we received a TTSSpeakFrame and the LLM response included text (it
+        # might be that it's only a function calling response) we pause
+        # processing more frames until we receive a BotStoppedSpeakingFrame.
+        if isinstance(frame, TTSSpeakFrame):
+            await self.pause_processing_frames()
+        elif isinstance(frame, LLMFullResponseEndFrame) and self._started:
+            await self.pause_processing_frames()
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.resume_processing_frames()
+
    async def _connect(self):
        await self._connect_websocket()

@@ -357,13 +370,8 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService):
        except Exception as e:
            logger.error(f"{self} error closing websocket: {e}")

-    def _get_websocket(self):
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
    async def _receive_messages(self):
-        async for message in self._get_websocket():
+        async for message in self._websocket:
            msg = json.loads(message)
            if msg.get("audio"):
                await self.stop_ttfb_metrics()
@@ -566,16 +574,18 @@ class ElevenLabsHttpTTSService(TTSService):
                    return

                await self.start_tts_usage_metrics(text)
-
                yield TTSStartedFrame()

                async for chunk in response.content:
                    if chunk:
                        await self.stop_ttfb_metrics()
                        yield TTSAudioRawFrame(chunk, self.sample_rate, 1)
+
+                yield TTSStoppedFrame()
+
        except Exception as e:
            logger.error(f"Error in run_tts: {e}")
            yield ErrorFrame(error=str(e))
+
        finally:
-            await self.stop_ttfb_metrics()
            yield TTSStoppedFrame()
--- a/src/pipecat/services/fal.py
+++ b/src/pipecat/services/fal.py
@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

-import asyncio
 import io
 import os
 from typing import AsyncGenerator, Dict, Optional, Union
@@ -54,11 +53,6 @@ class FalImageGenService(ImageGenService):
            os.environ["FAL_KEY"] = key

    async def run_image_gen(self, prompt: str) -> AsyncGenerator[Frame, None]:
-        def load_image_bytes(encoded_image: bytes):
-            buffer = io.BytesIO(encoded_image)
-            image = Image.open(buffer)
-            return (image.tobytes(), image.size, image.format)
-
        logger.debug(f"Generating image from prompt: {prompt}")

        response = await fal_client.run_async(
@@ -79,8 +73,10 @@ class FalImageGenService(ImageGenService):
        logger.debug(f"Downloading image {image_url} ...")
        async with self._aiohttp_session.get(image_url) as response:
            logger.debug(f"Downloaded image {image_url}")
-            encoded_image = await response.content.read()
-            (image_bytes, size, format) = await asyncio.to_thread(load_image_bytes, encoded_image)
+            image_stream = io.BytesIO(await response.content.read())
+            image = Image.open(image_stream)

-            frame = URLImageRawFrame(url=image_url, image=image_bytes, size=size, format=format)
+            frame = URLImageRawFrame(
+                url=image_url, image=image.tobytes(), size=image.size, format=image.format
+            )
            yield frame
--- a/src/pipecat/services/fireworks.py
+++ b/src/pipecat/services/fireworks.py
@@ -8,7 +8,6 @@
 from typing import List

 from loguru import logger
-from tenacity import retry, stop_after_attempt, wait_fixed

 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
@@ -51,7 +50,6 @@ class FireworksLLMService(OpenAILLMService):
        logger.debug(f"Creating Fireworks client with api {base_url}")
        return super().create_client(api_key, base_url, **kwargs)

-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(2))
    async def get_chat_completions(
        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
    ):
--- a/src/pipecat/services/fish.py
+++ b/src/pipecat/services/fish.py
@@ -11,13 +11,16 @@ from loguru import logger
 from pydantic import BaseModel

 from pipecat.frames.frames import (
+    BotStoppedSpeakingFrame,
    CancelFrame,
    EndFrame,
    ErrorFrame,
    Frame,
+    LLMFullResponseEndFrame,
    StartFrame,
    StartInterruptionFrame,
    TTSAudioRawFrame,
+    TTSSpeakFrame,
    TTSStartedFrame,
    TTSStoppedFrame,
 )
@@ -57,12 +60,7 @@ class FishAudioTTSService(TTSService, WebsocketService):
        params: InputParams = InputParams(),
        **kwargs,
    ):
-        super().__init__(
-            push_stop_frames=True,
-            pause_frame_processing=True,
-            sample_rate=sample_rate,
-            **kwargs,
-        )
+        super().__init__(sample_rate=sample_rate, **kwargs)

        self._api_key = api_key
        self._base_url = "wss://api.fish.audio/v1/tts/live"
@@ -168,6 +166,16 @@ class FishAudioTTSService(TTSService, WebsocketService):
            except Exception as e:
                logger.error(f"Error processing message: {e}")

+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TTSSpeakFrame):
+            await self.pause_processing_frames()
+        elif isinstance(frame, LLMFullResponseEndFrame) and self._request_id:
+            await self.pause_processing_frames()
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.resume_processing_frames()
+
    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
        await super()._handle_interruption(frame, direction)
        await self.stop_all_metrics()
--- a/src/pipecat/services/gemini_multimodal_live/gemini.py
+++ b/src/pipecat/services/gemini_multimodal_live/gemini.py
@@ -115,10 +115,10 @@ class GeminiMultimodalLiveUserContextAggregator(OpenAIUserContextAggregator):


 class GeminiMultimodalLiveAssistantContextAggregator(OpenAIAssistantContextAggregator):
-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        # We don't want to store any images in the context. Revisit this later when the API evolves.
        self._pending_image_frame_message = None
-        await super().push_aggregation()
+        await super()._push_aggregation()


@dataclass
@@ -706,6 +706,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
        GeminiMultimodalLiveContext.upgrade(context)
        user = GeminiMultimodalLiveUserContextAggregator(context)
        assistant = GeminiMultimodalLiveAssistantContextAggregator(
-            context, expect_stripped_words=assistant_expect_stripped_words
+            user, expect_stripped_words=assistant_expect_stripped_words
        )
        return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant)
--- a/src/pipecat/services/google/google.py
+++ b/src/pipecat/services/google/google.py
@@ -8,35 +8,24 @@ import asyncio
 import base64
 import io
 import json
-import os
-import time
-
-# Suppress gRPC fork warnings
-os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
-
 from dataclasses import dataclass
-from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional

 from loguru import logger
 from PIL import Image
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field

 from pipecat.frames.frames import (
    AudioRawFrame,
-    CancelFrame,
-    EndFrame,
    ErrorFrame,
    Frame,
    FunctionCallResultProperties,
-    InterimTranscriptionFrame,
    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
    LLMMessagesFrame,
    LLMTextFrame,
    LLMUpdateSettingsFrame,
    OpenAILLMContextAssistantTimestampFrame,
-    StartFrame,
-    TranscriptionFrame,
    TTSAudioRawFrame,
    TTSStartedFrame,
    TTSStoppedFrame,
@@ -49,7 +38,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContextFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import ImageGenService, LLMService, STTService, TTSService
+from pipecat.services.ai_services import ImageGenService, LLMService, TTSService
 from pipecat.services.google.frames import LLMSearchResponseFrame
 from pipecat.services.openai import (
    OpenAIAssistantContextAggregator,
@@ -62,13 +51,10 @@ try:
    import google.ai.generativelanguage as glm
    import google.generativeai as gai
    from google import genai
-    from google.api_core.client_options import ClientOptions
-    from google.cloud import speech_v2, texttospeech_v1
-    from google.cloud.speech_v2.types import cloud_speech
+    from google.cloud import texttospeech_v1
    from google.genai import types
    from google.generativeai.types import GenerationConfig
    from google.oauth2 import service_account
-
 except ModuleNotFoundError as e:
    logger.error(f"Exception: {e}")
    logger.error(
@@ -77,7 +63,7 @@ except ModuleNotFoundError as e:
    raise Exception(f"Missing module: {e}")


-def language_to_google_tts_language(language: Language) -> Optional[str]:
+def language_to_google_language(language: Language) -> Optional[str]:
    language_map = {
        # Afrikaans
        Language.AF: "af-ZA",
@@ -237,307 +223,8 @@ def language_to_google_tts_language(language: Language) -> Optional[str]:
    return language_map.get(language)


-def language_to_google_stt_language(language: Language) -> Optional[str]:
-    """Maps Language enum to Google Speech-to-Text V2 language codes.
-
-    Args:
-        language: Language enum value.
-
-    Returns:
-        Optional[str]: Google STT language code or None if not supported.
-    """
-    language_map = {
-        # Afrikaans
-        Language.AF: "af-ZA",
-        Language.AF_ZA: "af-ZA",
-        # Albanian
-        Language.SQ: "sq-AL",
-        Language.SQ_AL: "sq-AL",
-        # Amharic
-        Language.AM: "am-ET",
-        Language.AM_ET: "am-ET",
-        # Arabic
-        Language.AR: "ar-EG",  # Default to Egypt
-        Language.AR_AE: "ar-AE",
-        Language.AR_BH: "ar-BH",
-        Language.AR_DZ: "ar-DZ",
-        Language.AR_EG: "ar-EG",
-        Language.AR_IQ: "ar-IQ",
-        Language.AR_JO: "ar-JO",
-        Language.AR_KW: "ar-KW",
-        Language.AR_LB: "ar-LB",
-        Language.AR_MA: "ar-MA",
-        Language.AR_OM: "ar-OM",
-        Language.AR_QA: "ar-QA",
-        Language.AR_SA: "ar-SA",
-        Language.AR_SY: "ar-SY",
-        Language.AR_TN: "ar-TN",
-        Language.AR_YE: "ar-YE",
-        # Armenian
-        Language.HY: "hy-AM",
-        Language.HY_AM: "hy-AM",
-        # Azerbaijani
-        Language.AZ: "az-AZ",
-        Language.AZ_AZ: "az-AZ",
-        # Basque
-        Language.EU: "eu-ES",
-        Language.EU_ES: "eu-ES",
-        # Bengali
-        Language.BN: "bn-IN",  # Default to India
-        Language.BN_BD: "bn-BD",
-        Language.BN_IN: "bn-IN",
-        # Bosnian
-        Language.BS: "bs-BA",
-        Language.BS_BA: "bs-BA",
-        # Bulgarian
-        Language.BG: "bg-BG",
-        Language.BG_BG: "bg-BG",
-        # Burmese
-        Language.MY: "my-MM",
-        Language.MY_MM: "my-MM",
-        # Catalan
-        Language.CA: "ca-ES",
-        Language.CA_ES: "ca-ES",
-        # Chinese
-        Language.ZH: "cmn-Hans-CN",  # Default to Simplified Chinese
-        Language.ZH_CN: "cmn-Hans-CN",
-        Language.ZH_HK: "cmn-Hans-HK",
-        Language.ZH_TW: "cmn-Hant-TW",
-        Language.YUE: "yue-Hant-HK",  # Cantonese
-        Language.YUE_CN: "yue-Hant-HK",
-        # Croatian
-        Language.HR: "hr-HR",
-        Language.HR_HR: "hr-HR",
-        # Czech
-        Language.CS: "cs-CZ",
-        Language.CS_CZ: "cs-CZ",
-        # Danish
-        Language.DA: "da-DK",
-        Language.DA_DK: "da-DK",
-        # Dutch
-        Language.NL: "nl-NL",  # Default to Netherlands
-        Language.NL_BE: "nl-BE",
-        Language.NL_NL: "nl-NL",
-        # English
-        Language.EN: "en-US",  # Default to US
-        Language.EN_AU: "en-AU",
-        Language.EN_CA: "en-CA",
-        Language.EN_GB: "en-GB",
-        Language.EN_GH: "en-GH",
-        Language.EN_HK: "en-HK",
-        Language.EN_IN: "en-IN",
-        Language.EN_IE: "en-IE",
-        Language.EN_KE: "en-KE",
-        Language.EN_NG: "en-NG",
-        Language.EN_NZ: "en-NZ",
-        Language.EN_PH: "en-PH",
-        Language.EN_SG: "en-SG",
-        Language.EN_TZ: "en-TZ",
-        Language.EN_US: "en-US",
-        Language.EN_ZA: "en-ZA",
-        # Estonian
-        Language.ET: "et-EE",
-        Language.ET_EE: "et-EE",
-        # Filipino
-        Language.FIL: "fil-PH",
-        Language.FIL_PH: "fil-PH",
-        # Finnish
-        Language.FI: "fi-FI",
-        Language.FI_FI: "fi-FI",
-        # French
-        Language.FR: "fr-FR",  # Default to France
-        Language.FR_BE: "fr-BE",
-        Language.FR_CA: "fr-CA",
-        Language.FR_CH: "fr-CH",
-        Language.FR_FR: "fr-FR",
-        # Galician
-        Language.GL: "gl-ES",
-        Language.GL_ES: "gl-ES",
-        # Georgian
-        Language.KA: "ka-GE",
-        Language.KA_GE: "ka-GE",
-        # German
-        Language.DE: "de-DE",  # Default to Germany
-        Language.DE_AT: "de-AT",
-        Language.DE_CH: "de-CH",
-        Language.DE_DE: "de-DE",
-        # Greek
-        Language.EL: "el-GR",
-        Language.EL_GR: "el-GR",
-        # Gujarati
-        Language.GU: "gu-IN",
-        Language.GU_IN: "gu-IN",
-        # Hebrew
-        Language.HE: "iw-IL",
-        Language.HE_IL: "iw-IL",
-        # Hindi
-        Language.HI: "hi-IN",
-        Language.HI_IN: "hi-IN",
-        # Hungarian
-        Language.HU: "hu-HU",
-        Language.HU_HU: "hu-HU",
-        # Icelandic
-        Language.IS: "is-IS",
-        Language.IS_IS: "is-IS",
-        # Indonesian
-        Language.ID: "id-ID",
-        Language.ID_ID: "id-ID",
-        # Italian
-        Language.IT: "it-IT",
-        Language.IT_IT: "it-IT",
-        Language.IT_CH: "it-CH",
-        # Japanese
-        Language.JA: "ja-JP",
-        Language.JA_JP: "ja-JP",
-        # Javanese
-        Language.JV: "jv-ID",
-        Language.JV_ID: "jv-ID",
-        # Kannada
-        Language.KN: "kn-IN",
-        Language.KN_IN: "kn-IN",
-        # Kazakh
-        Language.KK: "kk-KZ",
-        Language.KK_KZ: "kk-KZ",
-        # Khmer
-        Language.KM: "km-KH",
-        Language.KM_KH: "km-KH",
-        # Korean
-        Language.KO: "ko-KR",
-        Language.KO_KR: "ko-KR",
-        # Lao
-        Language.LO: "lo-LA",
-        Language.LO_LA: "lo-LA",
-        # Latvian
-        Language.LV: "lv-LV",
-        Language.LV_LV: "lv-LV",
-        # Lithuanian
-        Language.LT: "lt-LT",
-        Language.LT_LT: "lt-LT",
-        # Macedonian
-        Language.MK: "mk-MK",
-        Language.MK_MK: "mk-MK",
-        # Malay
-        Language.MS: "ms-MY",
-        Language.MS_MY: "ms-MY",
-        # Malayalam
-        Language.ML: "ml-IN",
-        Language.ML_IN: "ml-IN",
-        # Marathi
-        Language.MR: "mr-IN",
-        Language.MR_IN: "mr-IN",
-        # Mongolian
-        Language.MN: "mn-MN",
-        Language.MN_MN: "mn-MN",
-        # Nepali
-        Language.NE: "ne-NP",
-        Language.NE_NP: "ne-NP",
-        # Norwegian
-        Language.NO: "no-NO",
-        Language.NB: "no-NO",
-        Language.NB_NO: "no-NO",
-        # Persian
-        Language.FA: "fa-IR",
-        Language.FA_IR: "fa-IR",
-        # Polish
-        Language.PL: "pl-PL",
-        Language.PL_PL: "pl-PL",
-        # Portuguese
-        Language.PT: "pt-PT",  # Default to Portugal
-        Language.PT_BR: "pt-BR",
-        Language.PT_PT: "pt-PT",
-        # Punjabi
-        Language.PA: "pa-Guru-IN",
-        Language.PA_IN: "pa-Guru-IN",
-        # Romanian
-        Language.RO: "ro-RO",
-        Language.RO_RO: "ro-RO",
-        # Russian
-        Language.RU: "ru-RU",
-        Language.RU_RU: "ru-RU",
-        # Serbian
-        Language.SR: "sr-RS",
-        Language.SR_RS: "sr-RS",
-        # Sinhala
-        Language.SI: "si-LK",
-        Language.SI_LK: "si-LK",
-        # Slovak
-        Language.SK: "sk-SK",
-        Language.SK_SK: "sk-SK",
-        # Slovenian
-        Language.SL: "sl-SI",
-        Language.SL_SI: "sl-SI",
-        # Spanish
-        Language.ES: "es-ES",  # Default to Spain
-        Language.ES_AR: "es-AR",
-        Language.ES_BO: "es-BO",
-        Language.ES_CL: "es-CL",
-        Language.ES_CO: "es-CO",
-        Language.ES_CR: "es-CR",
-        Language.ES_DO: "es-DO",
-        Language.ES_EC: "es-EC",
-        Language.ES_ES: "es-ES",
-        Language.ES_GT: "es-GT",
-        Language.ES_HN: "es-HN",
-        Language.ES_MX: "es-MX",
-        Language.ES_NI: "es-NI",
-        Language.ES_PA: "es-PA",
-        Language.ES_PE: "es-PE",
-        Language.ES_PR: "es-PR",
-        Language.ES_PY: "es-PY",
-        Language.ES_SV: "es-SV",
-        Language.ES_US: "es-US",
-        Language.ES_UY: "es-UY",
-        Language.ES_VE: "es-VE",
-        # Sundanese
-        Language.SU: "su-ID",
-        Language.SU_ID: "su-ID",
-        # Swahili
-        Language.SW: "sw-TZ",  # Default to Tanzania
-        Language.SW_KE: "sw-KE",
-        Language.SW_TZ: "sw-TZ",
-        # Swedish
-        Language.SV: "sv-SE",
-        Language.SV_SE: "sv-SE",
-        # Tamil
-        Language.TA: "ta-IN",  # Default to India
-        Language.TA_IN: "ta-IN",
-        Language.TA_MY: "ta-MY",
-        Language.TA_SG: "ta-SG",
-        Language.TA_LK: "ta-LK",
-        # Telugu
-        Language.TE: "te-IN",
-        Language.TE_IN: "te-IN",
-        # Thai
-        Language.TH: "th-TH",
-        Language.TH_TH: "th-TH",
-        # Turkish
-        Language.TR: "tr-TR",
-        Language.TR_TR: "tr-TR",
-        # Ukrainian
-        Language.UK: "uk-UA",
-        Language.UK_UA: "uk-UA",
-        # Urdu
-        Language.UR: "ur-IN",  # Default to India
-        Language.UR_IN: "ur-IN",
-        Language.UR_PK: "ur-PK",
-        # Uzbek
-        Language.UZ: "uz-UZ",
-        Language.UZ_UZ: "uz-UZ",
-        # Vietnamese
-        Language.VI: "vi-VN",
-        Language.VI_VN: "vi-VN",
-        # Xhosa
-        Language.XH: "xh-ZA",
-        # Zulu
-        Language.ZU: "zu-ZA",
-        Language.ZU_ZA: "zu-ZA",
-    }
-
-    return language_map.get(language)
-
-
 class GoogleUserContextAggregator(OpenAIUserContextAggregator):
-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if len(self._aggregation) > 0:
            self._context.add_message(
                glm.Content(role="user", parts=[glm.Part(text=self._aggregation)])
@@ -552,11 +239,11 @@ class GoogleUserContextAggregator(OpenAIUserContextAggregator):
            await self.push_frame(frame)

            # Reset our accumulator state.
-            self.reset()
+            self._reset()


 class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if not (
            self._aggregation or self._function_call_result or self._pending_image_frame_message
        ):
@@ -565,15 +252,10 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
        run_llm = False
        properties: Optional[FunctionCallResultProperties] = None

-        aggregation = self._aggregation.strip()
-        self.reset()
+        aggregation = self._aggregation
+        self._reset()

        try:
-            if aggregation:
-                self._context.add_message(
-                    glm.Content(role="model", parts=[glm.Part(text=aggregation)])
-                )
-
            if self._function_call_result:
                frame = self._function_call_result
                properties = frame.properties
@@ -613,6 +295,11 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
                    else:
                        # Default behavior is to run the LLM if there are no function calls in progress
                        run_llm = not bool(self._function_calls_in_progress)
+            else:
+                if aggregation.strip():
+                    self._context.add_message(
+                        glm.Content(role="model", parts=[glm.Part(text=aggregation)])
+                    )

            if self._pending_image_frame_message:
                frame = self._pending_image_frame_message
@@ -626,14 +313,15 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
                run_llm = True

            if run_llm:
-                await self.push_context_frame(FrameDirection.UPSTREAM)
+                await self._user_context_aggregator.push_context_frame()

            # Emit the on_context_updated callback once the function call result is added to the context
            if properties and properties.on_context_updated is not None:
                await properties.on_context_updated()

            # Push context frame
-            await self.push_context_frame()
+            frame = OpenAILLMContextFrame(self._context)
+            await self.push_frame(frame)

            # Push timestamp frame with current time
            timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601())
@@ -916,9 +604,9 @@ class GoogleLLMContext(OpenAILLMContext):
        # Check if we only have function-related messages (no regular text)
        has_regular_messages = any(
            len(msg.parts) == 1
-            and not getattr(msg.parts[0], "text", None)
-            and getattr(msg.parts[0], "function_call", None)
-            and getattr(msg.parts[0], "function_response", None)
+            and hasattr(msg.parts[0], "text")
+            and not hasattr(msg.parts[0], "function_call")
+            and not hasattr(msg.parts[0], "function_response")
            for msg in self._messages
        )

@@ -933,7 +621,7 @@ class GoogleLLMContext(OpenAILLMContext):


 class GoogleLLMService(LLMService):
-    """This class implements inference with Google's AI models.
+    """This class implements inference with Google's AI models

    This service translates internally from OpenAILLMContext to the messages format
    expected by the Google AI model. We are using the OpenAILLMContext as a lingua
@@ -1174,7 +862,7 @@ class GoogleLLMService(LLMService):
    ) -> GoogleContextAggregatorPair:
        user = GoogleUserContextAggregator(context)
        assistant = GoogleAssistantContextAggregator(
-            context, expect_stripped_words=assistant_expect_stripped_words
+            user, expect_stripped_words=assistant_expect_stripped_words
        )
        return GoogleContextAggregatorPair(_user=user, _assistant=assistant)

@@ -1239,7 +927,7 @@ class GoogleTTSService(TTSService):
        return True

    def language_to_service_language(self, language: Language) -> Optional[str]:
-        return language_to_google_tts_language(language)
+        return language_to_google_language(language)

    def _construct_ssml(self, text: str) -> str:
        ssml = "<speak>"
@@ -1409,451 +1097,3 @@ class GoogleImageGenService(ImageGenService):
        except Exception as e:
            logger.error(f"{self} error generating image: {e}")
            yield ErrorFrame(f"Image generation error: {str(e)}")
-
-
-class GoogleSTTService(STTService):
-    """Google Cloud Speech-to-Text V2 service implementation.
-
-    Provides real-time speech recognition using Google Cloud's Speech-to-Text V2 API
-    with streaming support. Handles audio transcription and optional voice activity detection.
-
-    Attributes:
-        InputParams: Configuration parameters for the STT service.
-    """
-
-    # Google Cloud's STT service has a connection time limit of 5 minutes per stream.
-    # They've shared an "endless streaming" example that guided this implementation:
-    # https://cloud.google.com/speech-to-text/docs/transcribe-streaming-audio#endless-streaming
-
-    STREAMING_LIMIT = 240000  # 4 minutes in milliseconds
-
-    class InputParams(BaseModel):
-        """Configuration parameters for Google Speech-to-Text.
-
-        Attributes:
-            languages: Single language or list of recognition languages. First language is primary.
-            model: Speech recognition model to use.
-            use_separate_recognition_per_channel: Process each audio channel separately.
-            enable_automatic_punctuation: Add punctuation to transcripts.
-            enable_spoken_punctuation: Include spoken punctuation in transcript.
-            enable_spoken_emojis: Include spoken emojis in transcript.
-            profanity_filter: Filter profanity from transcript.
-            enable_word_time_offsets: Include timing information for each word.
-            enable_word_confidence: Include confidence scores for each word.
-            enable_interim_results: Stream partial recognition results.
-            enable_voice_activity_events: Detect voice activity in audio.
-        """
-
-        languages: Union[Language, List[Language]] = Field(default_factory=lambda: [Language.EN_US])
-        model: Optional[str] = "latest_long"
-        use_separate_recognition_per_channel: Optional[bool] = False
-        enable_automatic_punctuation: Optional[bool] = True
-        enable_spoken_punctuation: Optional[bool] = False
-        enable_spoken_emojis: Optional[bool] = False
-        profanity_filter: Optional[bool] = False
-        enable_word_time_offsets: Optional[bool] = False
-        enable_word_confidence: Optional[bool] = False
-        enable_interim_results: Optional[bool] = True
-        enable_voice_activity_events: Optional[bool] = False
-
-        @field_validator("languages", mode="before")
-        @classmethod
-        def validate_languages(cls, v) -> List[Language]:
-            if isinstance(v, Language):
-                return [v]
-            return v
-
-        @property
-        def language_list(self) -> List[Language]:
-            """Get languages as a guaranteed list."""
-            assert isinstance(self.languages, list)
-            return self.languages
-
-    def __init__(
-        self,
-        *,
-        credentials: Optional[str] = None,
-        credentials_path: Optional[str] = None,
-        location: str = "global",
-        sample_rate: Optional[int] = None,
-        params: InputParams = InputParams(),
-        **kwargs,
-    ):
-        """Initialize the Google STT service.
-
-        Args:
-            credentials: JSON string containing Google Cloud service account credentials.
-            credentials_path: Path to service account credentials JSON file.
-            location: Google Cloud location (e.g., "global", "us-central1").
-            sample_rate: Audio sample rate in Hertz.
-            params: Configuration parameters for the service.
-            **kwargs: Additional arguments passed to STTService.
-
-        Raises:
-            ValueError: If neither credentials nor credentials_path is provided.
-            ValueError: If project ID is not found in credentials.
-        """
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        self._location = location
-        self._stream = None
-        self._config = None
-        self._request_queue = asyncio.Queue()
-        self._streaming_task = None
-
-        # Used for keep-alive logic
-        self._stream_start_time = 0
-        self._last_audio_input = []
-        self._audio_input = []
-        self._result_end_time = 0
-        self._is_final_end_time = 0
-        self._final_request_end_time = 0
-        self._bridging_offset = 0
-        self._last_transcript_was_final = False
-        self._new_stream = True
-        self._restart_counter = 0
-
-        # Configure client options based on location
-        client_options = None
-        if self._location != "global":
-            client_options = ClientOptions(api_endpoint=f"{self._location}-speech.googleapis.com")
-
-        # Extract project ID and create client
-        if credentials:
-            json_account_info = json.loads(credentials)
-            self._project_id = json_account_info.get("project_id")
-            creds = service_account.Credentials.from_service_account_info(json_account_info)
-        elif credentials_path:
-            with open(credentials_path) as f:
-                json_account_info = json.load(f)
-                self._project_id = json_account_info.get("project_id")
-            creds = service_account.Credentials.from_service_account_file(credentials_path)
-        else:
-            raise ValueError("Either credentials or credentials_path must be provided")
-
-        if not self._project_id:
-            raise ValueError("Project ID not found in credentials")
-
-        self._client = speech_v2.SpeechAsyncClient(credentials=creds, client_options=client_options)
-
-        self._settings = {
-            "language_codes": [
-                self.language_to_service_language(lang) for lang in params.language_list
-            ],
-            "model": params.model,
-            "use_separate_recognition_per_channel": params.use_separate_recognition_per_channel,
-            "enable_automatic_punctuation": params.enable_automatic_punctuation,
-            "enable_spoken_punctuation": params.enable_spoken_punctuation,
-            "enable_spoken_emojis": params.enable_spoken_emojis,
-            "profanity_filter": params.profanity_filter,
-            "enable_word_time_offsets": params.enable_word_time_offsets,
-            "enable_word_confidence": params.enable_word_confidence,
-            "enable_interim_results": params.enable_interim_results,
-            "enable_voice_activity_events": params.enable_voice_activity_events,
-        }
-
-    def language_to_service_language(self, language: Language | List[Language]) -> str | List[str]:
-        """Convert Language enum(s) to Google STT language code(s).
-
-        Args:
-            language: Single Language enum or list of Language enums.
-
-        Returns:
-            str | List[str]: Google STT language code(s).
-        """
-        if isinstance(language, list):
-            return [language_to_google_stt_language(lang) or "en-US" for lang in language]
-        return language_to_google_stt_language(language) or "en-US"
-
-    async def _reconnect_if_needed(self):
-        """Reconnect the stream if it's currently active."""
-        if self._streaming_task:
-            logger.debug("Reconnecting stream due to configuration changes")
-            await self._disconnect()
-            await self._connect()
-
-    async def set_languages(self, languages: List[Language]):
-        """Update the service's recognition languages.
-
-        Args:
-            languages: List of languages for recognition. First language is primary.
-        """
-        logger.debug(f"Switching STT languages to: {languages}")
-        self._settings["language_codes"] = [
-            self.language_to_service_language(lang) for lang in languages
-        ]
-        # Recreate stream with new languages
-        await self._reconnect_if_needed()
-
-    async def set_model(self, model: str):
-        """Update the service's recognition model."""
-        logger.debug(f"Switching STT model to: {model}")
-        await super().set_model(model)
-        self._settings["model"] = model
-        # Recreate stream with new model
-        await self._reconnect_if_needed()
-
-    async def start(self, frame: StartFrame):
-        await super().start(frame)
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def update_options(
-        self,
-        *,
-        languages: Optional[List[Language]] = None,
-        model: Optional[str] = None,
-        enable_automatic_punctuation: Optional[bool] = None,
-        enable_spoken_punctuation: Optional[bool] = None,
-        enable_spoken_emojis: Optional[bool] = None,
-        profanity_filter: Optional[bool] = None,
-        enable_word_time_offsets: Optional[bool] = None,
-        enable_word_confidence: Optional[bool] = None,
-        enable_interim_results: Optional[bool] = None,
-        enable_voice_activity_events: Optional[bool] = None,
-        location: Optional[str] = None,
-    ) -> None:
-        """Update service options dynamically.
-
-        Args:
-            languages: New list of recongition languages.
-            model: New recognition model.
-            enable_automatic_punctuation: Enable/disable automatic punctuation.
-            enable_spoken_punctuation: Enable/disable spoken punctuation.
-            enable_spoken_emojis: Enable/disable spoken emojis.
-            profanity_filter: Enable/disable profanity filter.
-            enable_word_time_offsets: Enable/disable word timing info.
-            enable_word_confidence: Enable/disable word confidence scores.
-            enable_interim_results: Enable/disable interim results.
-            enable_voice_activity_events: Enable/disable voice activity detection.
-            location: New Google Cloud location.
-
-        Note:
-            Changes that affect the streaming configuration will cause
-            the stream to be reconnected.
-        """
-        # Update settings with new values
-        if languages is not None:
-            logger.debug(f"Updating language to: {languages}")
-            self._settings["language_codes"] = [
-                self.language_to_service_language(lang) for lang in languages
-            ]
-
-        if model is not None:
-            logger.debug(f"Updating model to: {model}")
-            self._settings["model"] = model
-
-        if enable_automatic_punctuation is not None:
-            logger.debug(f"Updating automatic punctuation to: {enable_automatic_punctuation}")
-            self._settings["enable_automatic_punctuation"] = enable_automatic_punctuation
-
-        if enable_spoken_punctuation is not None:
-            logger.debug(f"Updating spoken punctuation to: {enable_spoken_punctuation}")
-            self._settings["enable_spoken_punctuation"] = enable_spoken_punctuation
-
-        if enable_spoken_emojis is not None:
-            logger.debug(f"Updating spoken emojis to: {enable_spoken_emojis}")
-            self._settings["enable_spoken_emojis"] = enable_spoken_emojis
-
-        if profanity_filter is not None:
-            logger.debug(f"Updating profanity filter to: {profanity_filter}")
-            self._settings["profanity_filter"] = profanity_filter
-
-        if enable_word_time_offsets is not None:
-            logger.debug(f"Updating word time offsets to: {enable_word_time_offsets}")
-            self._settings["enable_word_time_offsets"] = enable_word_time_offsets
-
-        if enable_word_confidence is not None:
-            logger.debug(f"Updating word confidence to: {enable_word_confidence}")
-            self._settings["enable_word_confidence"] = enable_word_confidence
-
-        if enable_interim_results is not None:
-            logger.debug(f"Updating interim results to: {enable_interim_results}")
-            self._settings["enable_interim_results"] = enable_interim_results
-
-        if enable_voice_activity_events is not None:
-            logger.debug(f"Updating voice activity events to: {enable_voice_activity_events}")
-            self._settings["enable_voice_activity_events"] = enable_voice_activity_events
-
-        if location is not None:
-            logger.debug(f"Updating location to: {location}")
-            self._location = location
-
-        # Reconnect the stream for updates
-        await self._reconnect_if_needed()
-
-    async def _connect(self):
-        """Initialize streaming recognition config and stream."""
-        logger.debug("Connecting to Google Speech-to-Text")
-
-        # Set stream start time
-        self._stream_start_time = int(time.time() * 1000)
-        self._new_stream = True
-
-        self._config = cloud_speech.StreamingRecognitionConfig(
-            config=cloud_speech.RecognitionConfig(
-                explicit_decoding_config=cloud_speech.ExplicitDecodingConfig(
-                    encoding=cloud_speech.ExplicitDecodingConfig.AudioEncoding.LINEAR16,
-                    sample_rate_hertz=self.sample_rate,
-                    audio_channel_count=1,
-                ),
-                language_codes=self._settings["language_codes"],
-                model=self._settings["model"],
-                features=cloud_speech.RecognitionFeatures(
-                    enable_automatic_punctuation=self._settings["enable_automatic_punctuation"],
-                    enable_spoken_punctuation=self._settings["enable_spoken_punctuation"],
-                    enable_spoken_emojis=self._settings["enable_spoken_emojis"],
-                    profanity_filter=self._settings["profanity_filter"],
-                    enable_word_time_offsets=self._settings["enable_word_time_offsets"],
-                    enable_word_confidence=self._settings["enable_word_confidence"],
-                ),
-            ),
-            streaming_features=cloud_speech.StreamingRecognitionFeatures(
-                enable_voice_activity_events=self._settings["enable_voice_activity_events"],
-                interim_results=self._settings["enable_interim_results"],
-            ),
-        )
-
-        self._streaming_task = self.create_task(self._stream_audio())
-
-    async def _disconnect(self):
-        """Clean up streaming recognition resources."""
-        if self._streaming_task:
-            logger.debug("Disconnecting from Google Speech-to-Text")
-            # Send sentinel value to stop request generator
-            await self._request_queue.put(None)
-            await self.cancel_task(self._streaming_task)
-            self._streaming_task = None
-            # Clear any remaining items in the queue
-            while not self._request_queue.empty():
-                try:
-                    self._request_queue.get_nowait()
-                    self._request_queue.task_done()
-                except asyncio.QueueEmpty:
-                    break
-
-    async def _request_generator(self):
-        """Generates requests for the streaming recognize method."""
-        recognizer_path = f"projects/{self._project_id}/locations/{self._location}/recognizers/_"
-        logger.trace(f"Using recognizer path: {recognizer_path}")
-
-        try:
-            # Send initial config
-            yield cloud_speech.StreamingRecognizeRequest(
-                recognizer=recognizer_path,
-                streaming_config=self._config,
-            )
-
-            while True:
-                try:
-                    audio_data = await self._request_queue.get()
-                    if audio_data is None:  # Sentinel value to stop
-                        break
-
-                    # Check streaming limit
-                    if (int(time.time() * 1000) - self._stream_start_time) > self.STREAMING_LIMIT:
-                        logger.debug("Streaming limit reached, initiating graceful reconnection")
-                        # Instead of immediate reconnection, we'll break and let the stream close naturally
-                        self._last_audio_input = self._audio_input
-                        self._audio_input = []
-                        self._restart_counter += 1
-                        # Put the current audio chunk back in the queue
-                        await self._request_queue.put(audio_data)
-                        break
-
-                    self._audio_input.append(audio_data)
-                    yield cloud_speech.StreamingRecognizeRequest(audio=audio_data)
-
-                except asyncio.CancelledError:
-                    break
-                finally:
-                    self._request_queue.task_done()
-
-        except Exception as e:
-            logger.error(f"Error in request generator: {e}")
-            raise
-
-    async def _stream_audio(self):
-        """Handle bi-directional streaming with Google STT."""
-        try:
-            while True:
-                try:
-                    # Start bi-directional streaming
-                    streaming_recognize = await self._client.streaming_recognize(
-                        requests=self._request_generator()
-                    )
-
-                    # Process responses
-                    await self._process_responses(streaming_recognize)
-
-                    # If we're here, check if we need to reconnect
-                    if (int(time.time() * 1000) - self._stream_start_time) > self.STREAMING_LIMIT:
-                        logger.debug("Reconnecting stream after timeout")
-                        # Reset stream start time
-                        self._stream_start_time = int(time.time() * 1000)
-                        continue
-                    else:
-                        # Normal stream end
-                        break
-
-                except Exception as e:
-                    logger.error(f"Stream error, attempting to reconnect: {e}")
-                    await asyncio.sleep(1)  # Brief delay before reconnecting
-                    self._stream_start_time = int(time.time() * 1000)
-                    continue
-
-        except Exception as e:
-            logger.error(f"Error in streaming task: {e}")
-            await self.push_frame(ErrorFrame(str(e)))
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        """Process an audio chunk for STT transcription."""
-        if self._streaming_task:
-            # Queue the audio data
-            await self._request_queue.put(audio)
-        yield None
-
-    async def _process_responses(self, streaming_recognize):
-        """Process streaming recognition responses."""
-        try:
-            async for response in streaming_recognize:
-                # Check streaming limit
-                if (int(time.time() * 1000) - self._stream_start_time) > self.STREAMING_LIMIT:
-                    logger.debug("Stream timeout reached in response processing")
-                    break
-
-                if not response.results:
-                    continue
-
-                for result in response.results:
-                    if not result.alternatives:
-                        continue
-
-                    transcript = result.alternatives[0].transcript
-                    if not transcript:
-                        continue
-
-                    primary_language = self._settings["language_codes"][0]
-
-                    if result.is_final:
-                        self._last_transcript_was_final = True
-                        await self.push_frame(
-                            TranscriptionFrame(transcript, "", time_now_iso8601(), primary_language)
-                        )
-                    else:
-                        self._last_transcript_was_final = False
-                        await self.push_frame(
-                            InterimTranscriptionFrame(
-                                transcript, "", time_now_iso8601(), primary_language
-                            )
-                        )
-
-        except Exception as e:
-            logger.error(f"Error processing Google STT responses: {e}")
--- a/src/pipecat/services/grok.py
+++ b/src/pipecat/services/grok.py
@@ -17,7 +17,6 @@ from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContext,
    OpenAILLMContextFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.openai import (
    OpenAIAssistantContextAggregator,
    OpenAILLMService,
@@ -28,7 +27,7 @@ from pipecat.services.openai import (
 class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
    """Custom assistant context aggregator for Grok that handles empty content requirement."""

-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if not (
            self._aggregation or self._function_call_result or self._pending_image_frame_message
        ):
@@ -37,13 +36,10 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
        run_llm = False
        properties: Optional[FunctionCallResultProperties] = None

-        aggregation = self._aggregation.strip()
-        self.reset()
+        aggregation = self._aggregation
+        self._reset()

        try:
-            if aggregation:
-                self._context.add_message({"role": "assistant", "content": aggregation})
-
            if self._function_call_result:
                frame = self._function_call_result
                properties = frame.properties
@@ -80,6 +76,9 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
                        # Default behavior is to run the LLM if there are no function calls in progress
                        run_llm = not bool(self._function_calls_in_progress)

+            else:
+                self._context.add_message({"role": "assistant", "content": aggregation})
+
            if self._pending_image_frame_message:
                frame = self._pending_image_frame_message
                self._pending_image_frame_message = None
@@ -92,13 +91,14 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
                run_llm = True

            if run_llm:
-                await self.push_context_frame(FrameDirection.UPSTREAM)
+                await self._user_context_aggregator.push_context_frame()

            # Emit the on_context_updated callback once the function call result is added to the context
            if properties and properties.on_context_updated is not None:
                await properties.on_context_updated()

-            await self.push_context_frame()
+            frame = OpenAILLMContextFrame(self._context)
+            await self.push_frame(frame)

        except Exception as e:
            logger.error(f"Error processing frame: {e}")
@@ -212,6 +212,6 @@ class GrokLLMService(OpenAILLMService):
    ) -> GrokContextAggregatorPair:
        user = OpenAIUserContextAggregator(context)
        assistant = GrokAssistantContextAggregator(
-            context, expect_stripped_words=assistant_expect_stripped_words
+            user, expect_stripped_words=assistant_expect_stripped_words
        )
        return GrokContextAggregatorPair(_user=user, _assistant=assistant)
--- a/src/pipecat/services/groq.py
+++ b/src/pipecat/services/groq.py
@@ -54,8 +54,6 @@ class GroqSTTService(BaseWhisperSTTService):
        api_key: Groq API key. Defaults to None.
        base_url: API base URL. Defaults to "https://api.groq.com/openai/v1".
        language: Language of the audio input. Defaults to English.
-        prompt: Optional text to guide the model's style or continue a previous segment.
-        temperature: Optional sampling temperature between 0 and 1. Defaults to 0.0.
        **kwargs: Additional arguments passed to BaseWhisperSTTService.
    """

@@ -66,35 +64,17 @@ class GroqSTTService(BaseWhisperSTTService):
        api_key: Optional[str] = None,
        base_url: str = "https://api.groq.com/openai/v1",
        language: Optional[Language] = Language.EN,
-        prompt: Optional[str] = None,
-        temperature: Optional[float] = None,
        **kwargs,
    ):
        super().__init__(
-            model=model,
-            api_key=api_key,
-            base_url=base_url,
-            language=language,
-            prompt=prompt,
-            temperature=temperature,
-            **kwargs,
+            model=model, api_key=api_key, base_url=base_url, language=language, **kwargs
        )

    async def _transcribe(self, audio: bytes) -> Transcription:
        assert self._language is not None  # Assigned in the BaseWhisperSTTService class
-
-        # Build kwargs dict with only set parameters
-        kwargs = {
-            "file": ("audio.wav", audio, "audio/wav"),
-            "model": self.model_name,
-            "response_format": "json",
-            "language": self._language,
-        }
-
-        if self._prompt is not None:
-            kwargs["prompt"] = self._prompt
-
-        if self._temperature is not None:
-            kwargs["temperature"] = self._temperature
-
-        return await self._client.audio.transcriptions.create(**kwargs)
+        return await self._client.audio.transcriptions.create(
+            file=("audio.wav", audio, "audio/wav"),
+            model=self.model_name,
+            response_format="json",
+            language=self._language,
+        )
--- a/src/pipecat/services/lmnt.py
+++ b/src/pipecat/services/lmnt.py
@@ -73,7 +73,6 @@ class LmntTTSService(TTSService, WebsocketService):
        TTSService.__init__(
            self,
            push_stop_frames=True,
-            pause_frame_processing=True,
            sample_rate=sample_rate,
            **kwargs,
        )
--- a/src/pipecat/services/openai.py
+++ b/src/pipecat/services/openai.py
@@ -15,7 +15,6 @@ import httpx
 from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
-from tenacity import retry, stop_after_attempt, wait_fixed

 from pipecat.frames.frames import (
    ErrorFrame,
@@ -111,7 +110,7 @@ class BaseOpenAILLMService(LLMService):
        seed: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=0)
        temperature: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=2.0)
        # Note: top_k is currently not supported by the OpenAI client library,
-        # so top_k is ignored right now.
+        # so top_k is ignore right now.
        top_k: Optional[int] = Field(default=None, ge=0)
        top_p: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
        max_tokens: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=1)
@@ -161,7 +160,6 @@ class BaseOpenAILLMService(LLMService):
    def can_generate_metrics(self) -> bool:
        return True

-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(2))
    async def get_chat_completions(
        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
    ) -> AsyncStream[ChatCompletionChunk]:
@@ -268,6 +266,7 @@ class BaseOpenAILLMService(LLMService):
                if tool_call.function and tool_call.function.name:
                    function_name += tool_call.function.name
                    tool_call_id = tool_call.id
+                    await self.call_start_function(context, function_name)
                if tool_call.function and tool_call.function.arguments:
                    # Keep iterating through the response to collect all the argument fragments
                    arguments += tool_call.function.arguments
@@ -356,7 +355,7 @@ class OpenAILLMService(BaseOpenAILLMService):
    ) -> OpenAIContextAggregatorPair:
        user = OpenAIUserContextAggregator(context)
        assistant = OpenAIAssistantContextAggregator(
-            context, expect_stripped_words=assistant_expect_stripped_words
+            user, expect_stripped_words=assistant_expect_stripped_words
        )
        return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)

@@ -409,8 +408,6 @@ class OpenAISTTService(BaseWhisperSTTService):
        api_key: OpenAI API key. Defaults to None.
        base_url: API base URL. Defaults to None.
        language: Language of the audio input. Defaults to English.
-        prompt: Optional text to guide the model's style or continue a previous segment.
-        temperature: Optional sampling temperature between 0 and 1. Defaults to 0.0.
        **kwargs: Additional arguments passed to BaseWhisperSTTService.
    """

@@ -421,37 +418,17 @@ class OpenAISTTService(BaseWhisperSTTService):
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        language: Optional[Language] = Language.EN,
-        prompt: Optional[str] = None,
-        temperature: Optional[float] = None,
        **kwargs,
    ):
        super().__init__(
-            model=model,
-            api_key=api_key,
-            base_url=base_url,
-            language=language,
-            prompt=prompt,
-            temperature=temperature,
-            **kwargs,
+            model=model, api_key=api_key, base_url=base_url, language=language, **kwargs
        )

    async def _transcribe(self, audio: bytes) -> Transcription:
        assert self._language is not None  # Assigned in the BaseWhisperSTTService class
-
-        # Build kwargs dict with only set parameters
-        kwargs = {
-            "file": ("audio.wav", audio, "audio/wav"),
-            "model": self.model_name,
-            "language": self._language,
-        }
-
-        if self._prompt is not None:
-            kwargs["prompt"] = self._prompt
-
-        if self._temperature is not None:
-            kwargs["temperature"] = self._temperature
-
-        return await self._client.audio.transcriptions.create(**kwargs)
+        return await self._client.audio.transcriptions.create(
+            file=("audio.wav", audio, "audio/wav"), model=self.model_name, language=self._language
+        )


 class OpenAITTSService(TTSService):
@@ -556,8 +533,8 @@ class OpenAIImageMessageFrame(Frame):


 class OpenAIUserContextAggregator(LLMUserContextAggregator):
-    def __init__(self, context: OpenAILLMContext, **kwargs):
-        super().__init__(context=context, **kwargs)
+    def __init__(self, context: OpenAILLMContext):
+        super().__init__(context=context)

    async def process_frame(self, frame, direction):
        await super().process_frame(frame, direction)
@@ -593,8 +570,9 @@ class OpenAIUserContextAggregator(LLMUserContextAggregator):


 class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
-    def __init__(self, context: OpenAILLMContext, **kwargs):
-        super().__init__(context=context, **kwargs)
+    def __init__(self, user_context_aggregator: OpenAIUserContextAggregator, **kwargs):
+        super().__init__(context=user_context_aggregator._context, **kwargs)
+        self._user_context_aggregator = user_context_aggregator
        self._function_calls_in_progress = {}
        self._function_call_result = None
        self._pending_image_frame_message = None
@@ -614,7 +592,7 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
                del self._function_calls_in_progress[frame.tool_call_id]
                self._function_call_result = frame
                # TODO-CB: Kwin wants us to refactor this out of here but I REFUSE
-                await self.push_aggregation()
+                await self._push_aggregation()
            else:
                logger.warning(
                    "FunctionCallResultFrame tool_call_id does not match any function call in progress"
@@ -622,9 +600,9 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
                self._function_call_result = None
        elif isinstance(frame, OpenAIImageMessageFrame):
            self._pending_image_frame_message = frame
-            await self.push_aggregation()
+            await self._push_aggregation()

-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        if not (
            self._aggregation or self._function_call_result or self._pending_image_frame_message
        ):
@@ -633,13 +611,10 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
        run_llm = False
        properties: Optional[FunctionCallResultProperties] = None

-        aggregation = self._aggregation.strip()
-        self.reset()
+        aggregation = self._aggregation
+        self._reset()

        try:
-            if aggregation:
-                self._context.add_message({"role": "assistant", "content": aggregation})
-
            if self._function_call_result:
                frame = self._function_call_result
                properties = frame.properties
@@ -674,6 +649,9 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
                        # Default behavior is to run the LLM if there are no function calls in progress
                        run_llm = not bool(self._function_calls_in_progress)

+            else:
+                self._context.add_message({"role": "assistant", "content": aggregation})
+
            if self._pending_image_frame_message:
                frame = self._pending_image_frame_message
                self._pending_image_frame_message = None
@@ -686,14 +664,15 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
                run_llm = True

            if run_llm:
-                await self.push_context_frame(FrameDirection.UPSTREAM)
+                await self._user_context_aggregator.push_context_frame()

            # Emit the on_context_updated callback once the function call result is added to the context
            if properties and properties.on_context_updated is not None:
                await properties.on_context_updated()

            # Push context frame
-            await self.push_context_frame()
+            frame = OpenAILLMContextFrame(self._context)
+            await self.push_frame(frame)

            # Push timestamp frame with current time
            timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601())
--- a/src/pipecat/services/openai_realtime_beta/context.py
+++ b/src/pipecat/services/openai_realtime_beta/context.py
@@ -166,7 +166,7 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator):
        if isinstance(frame, LLMSetToolsFrame):
            await self.push_frame(frame, direction)

-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        # for the moment, ignore all user input coming into the pipeline.
        # todo: think about whether/how to fix this to allow for text input from
        #       upstream (transport/transcription, or other sources)
@@ -174,7 +174,7 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator):


 class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator):
-    async def push_aggregation(self):
+    async def _push_aggregation(self):
        # the only thing we implement here is function calling. in all other cases, messages
        # are added to the context when we receive openai realtime api events
        if not self._function_call_result:
@@ -182,7 +182,7 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)

        properties: Optional[FunctionCallResultProperties] = None

-        self.reset()
+        self._reset()
        try:
            run_llm = True
            frame = self._function_call_result
@@ -217,8 +217,8 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)
                # The standard function callback code path pushes the FunctionCallResultFrame from the llm itself,
                # so we didn't have a chance to add the result to the openai realtime api context. Let's push a
                # special frame to do that.
-                await self.push_frame(
-                    RealtimeFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM
+                await self._user_context_aggregator.push_frame(
+                    RealtimeFunctionCallResultFrame(result_frame=frame)
                )
                if properties and properties.run_llm is not None:
                    # If the tool call result has a run_llm property, use it
@@ -228,13 +228,14 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)
                    run_llm = not bool(self._function_calls_in_progress)

            if run_llm:
-                await self.push_context_frame(FrameDirection.UPSTREAM)
+                await self._user_context_aggregator.push_context_frame()

            # Emit the on_context_updated callback once the function call result is added to the context
            if properties and properties.on_context_updated is not None:
                await properties.on_context_updated()

-            await self.push_context_frame()
+            frame = OpenAILLMContextFrame(self._context)
+            await self.push_frame(frame)

        except Exception as e:
            logger.error(f"Error processing frame: {e}")
--- a/src/pipecat/services/openai_realtime_beta/openai.py
+++ b/src/pipecat/services/openai_realtime_beta/openai.py
@@ -568,6 +568,6 @@ class OpenAIRealtimeBetaLLMService(LLMService):
        OpenAIRealtimeLLMContext.upgrade_to_realtime(context)
        user = OpenAIRealtimeUserContextAggregator(context)
        assistant = OpenAIRealtimeAssistantContextAggregator(
-            context, expect_stripped_words=assistant_expect_stripped_words
+            user, expect_stripped_words=assistant_expect_stripped_words
        )
        return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
--- a/src/pipecat/services/openpipe.py
+++ b/src/pipecat/services/openpipe.py
@@ -7,7 +7,6 @@
 from typing import Dict, List, Optional

 from loguru import logger
-from tenacity import retry, stop_after_attempt, wait_fixed

 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
@@ -56,7 +55,6 @@ class OpenPipeLLMService(OpenAILLMService):
        )
        return client

-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(2))
    async def get_chat_completions(
        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
    ) -> AsyncStream[ChatCompletionChunk]:
--- a/src/pipecat/services/perplexity.py
+++ b/src/pipecat/services/perplexity.py
@@ -7,7 +7,6 @@
 from typing import List

 from loguru import logger
-from tenacity import retry, stop_after_attempt, wait_fixed

 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -57,7 +56,6 @@ class PerplexityLLMService(OpenAILLMService):
        self._has_reported_prompt_tokens = False
        self._is_processing = False

-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(2))
    async def get_chat_completions(
        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
    ) -> AsyncStream[ChatCompletionChunk]:
--- a/src/pipecat/services/playht.py
+++ b/src/pipecat/services/playht.py
@@ -120,7 +120,6 @@ class PlayHTTTSService(TTSService, WebsocketService):
    ):
        TTSService.__init__(
            self,
-            pause_frame_processing=True,
            sample_rate=sample_rate,
            **kwargs,
        )
@@ -270,6 +269,19 @@ class PlayHTTTSService(TTSService, WebsocketService):
                except json.JSONDecodeError:
                    logger.error(f"Invalid JSON message: {message}")

+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        # If we received a TTSSpeakFrame and the LLM response included text (it
+        # might be that it's only a function calling response) we pause
+        # processing more frames until we receive a BotStoppedSpeakingFrame.
+        if isinstance(frame, TTSSpeakFrame):
+            await self.pause_processing_frames()
+        elif isinstance(frame, LLMFullResponseEndFrame) and self._request_id:
+            await self.pause_processing_frames()
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.resume_processing_frames()
+
    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
        logger.debug(f"Generating TTS: [{text}]")

@@ -397,7 +409,6 @@ class PlayHTHttpTTSService(TTSService):
            await self.start_tts_usage_metrics(text)

            yield TTSStartedFrame()
-
            async for chunk in playht_gen:
                # skip the RIFF header.
                if in_header:
@@ -417,8 +428,6 @@ class PlayHTHttpTTSService(TTSService):
                        await self.stop_ttfb_metrics()
                        frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
                        yield frame
+            yield TTSStoppedFrame()
        except Exception as e:
            logger.error(f"{self} error generating TTS: {e}")
-        finally:
-            await self.stop_ttfb_metrics()
-            yield TTSStoppedFrame()
--- a/src/pipecat/services/rime.py
+++ b/src/pipecat/services/rime.py
@@ -4,9 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

-import base64
-import json
-import uuid
 from typing import AsyncGenerator, Optional

 import aiohttp
@@ -14,323 +11,13 @@ from loguru import logger
 from pydantic import BaseModel

 from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
    ErrorFrame,
    Frame,
-    StartFrame,
-    StartInterruptionFrame,
    TTSAudioRawFrame,
    TTSStartedFrame,
    TTSStoppedFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import AudioContextWordTTSService, TTSService
-from pipecat.services.websocket_service import WebsocketService
-from pipecat.transcriptions.language import Language
-
-try:
-    import websockets
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use Rime, you need to `pip install pipecat-ai[rime]`. Also, set `RIME_API_KEY` environment variable."
-    )
-    raise Exception(f"Missing module: {e}")
-
-
-def language_to_rime_language(language: Language) -> str:
-    """Convert pipecat Language to Rime language code.
-
-    Args:
-        language: The pipecat Language enum value.
-
-    Returns:
-        str: Three-letter language code used by Rime (e.g., 'eng' for English).
-    """
-    LANGUAGE_MAP = {
-        Language.EN: "eng",
-        Language.ES: "spa",
-    }
-    return LANGUAGE_MAP.get(language, "eng")
-
-
-class RimeTTSService(AudioContextWordTTSService, WebsocketService):
-    """Text-to-Speech service using Rime's websocket API.
-
-    Uses Rime's websocket JSON API to convert text to speech with word-level timing
-    information. Supports interruptions and maintains context across multiple messages
-    within a turn.
-    """
-
-    class InputParams(BaseModel):
-        """Configuration parameters for Rime TTS service."""
-
-        language: Optional[Language] = Language.EN
-        speed_alpha: Optional[float] = 1.0
-        reduce_latency: Optional[bool] = False
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        voice_id: str,
-        url: str = "wss://users-ws.rime.ai/ws2",
-        model: str = "mistv2",
-        sample_rate: Optional[int] = None,
-        params: InputParams = InputParams(),
-        **kwargs,
-    ):
-        """Initialize Rime TTS service.
-
-        Args:
-            api_key: Rime API key for authentication.
-            voice_id: ID of the voice to use.
-            url: Rime websocket API endpoint.
-            model: Model ID to use for synthesis.
-            sample_rate: Audio sample rate in Hz.
-            params: Additional configuration parameters.
-        """
-        # Initialize with parent class settings for proper frame handling
-        AudioContextWordTTSService.__init__(
-            self,
-            aggregate_sentences=True,
-            push_text_frames=False,
-            push_stop_frames=True,
-            pause_frame_processing=True,
-            sample_rate=sample_rate,
-            **kwargs,
-        )
-        WebsocketService.__init__(self)
-
-        # Store service configuration
-        self._api_key = api_key
-        self._url = url
-        self._voice_id = voice_id
-        self._model = model
-        self._settings = {
-            "speaker": voice_id,
-            "modelId": model,
-            "audioFormat": "pcm",
-            "samplingRate": 0,
-            "lang": self.language_to_service_language(params.language)
-            if params.language
-            else "eng",
-            "speedAlpha": params.speed_alpha,
-            "reduceLatency": params.reduce_latency,
-        }
-
-        # State tracking
-        self._context_id = None  # Tracks current turn
-        self._receive_task = None
-        self._cumulative_time = 0  # Accumulates time across messages
-
-    def can_generate_metrics(self) -> bool:
-        return True
-
-    def language_to_service_language(self, language: Language) -> str | None:
-        """Convert pipecat language to Rime language code."""
-        return language_to_rime_language(language)
-
-    async def set_model(self, model: str):
-        """Update the TTS model."""
-        self._model = model
-        await super().set_model(model)
-
-    def _build_msg(self, text: str = "") -> dict:
-        """Build JSON message for Rime API."""
-        return {"text": text, "contextId": self._context_id}
-
-    def _build_clear_msg(self) -> dict:
-        """Build clear operation message."""
-        return {"operation": "clear"}
-
-    def _build_eos_msg(self) -> dict:
-        """Build end-of-stream operation message."""
-        return {"operation": "eos"}
-
-    async def start(self, frame: StartFrame):
-        """Start the service and establish websocket connection."""
-        await super().start(frame)
-        self._settings["samplingRate"] = self.sample_rate
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the service and close connection."""
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel current operation and clean up."""
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def _connect(self):
-        """Establish websocket connection and start receive task."""
-        await self._connect_websocket()
-        self._receive_task = self.create_task(self._receive_task_handler(self.push_error))
-
-    async def _disconnect(self):
-        """Close websocket connection and clean up tasks."""
-        await self._disconnect_websocket()
-        if self._receive_task:
-            await self.cancel_task(self._receive_task)
-            self._receive_task = None
-
-    async def _connect_websocket(self):
-        """Connect to Rime websocket API with configured settings."""
-        try:
-            params = "&".join(f"{k}={v}" for k, v in self._settings.items())
-            url = f"{self._url}?{params}"
-            headers = {"Authorization": f"Bearer {self._api_key}"}
-            self._websocket = await websockets.connect(url, extra_headers=headers)
-        except Exception as e:
-            logger.error(f"{self} initialization error: {e}")
-            self._websocket = None
-
-    async def _disconnect_websocket(self):
-        """Close websocket connection and reset state."""
-        try:
-            await self.stop_all_metrics()
-            if self._websocket:
-                await self._websocket.send(json.dumps(self._build_eos_msg()))
-                await self._websocket.close()
-                self._websocket = None
-            self._context_id = None
-        except Exception as e:
-            logger.error(f"{self} error closing websocket: {e}")
-
-    def _get_websocket(self):
-        """Get active websocket connection or raise exception."""
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
-        """Handle interruption by clearing current context."""
-        await super()._handle_interruption(frame, direction)
-        await self.stop_all_metrics()
-        if self._context_id:
-            await self._get_websocket().send(json.dumps(self._build_clear_msg()))
-            self._context_id = None
-
-    def _calculate_word_times(self, words: list, starts: list, ends: list) -> list:
-        """Calculate word timing pairs with proper spacing and punctuation.
-
-        Args:
-            words: List of words from Rime.
-            starts: List of start times for each word.
-            ends: List of end times for each word.
-
-        Returns:
-            List of (word, timestamp) pairs with proper timing.
-        """
-        word_pairs = []
-        for i, (word, start_time, _) in enumerate(zip(words, starts, ends)):
-            if not word.strip():
-                continue
-
-            # Adjust timing by adding cumulative time
-            adjusted_start = start_time + self._cumulative_time
-
-            # Handle punctuation by appending to previous word
-            is_punctuation = bool(word.strip(",.!?") == "")
-            if is_punctuation and word_pairs:
-                prev_word, prev_time = word_pairs[-1]
-                word_pairs[-1] = (prev_word + word, prev_time)
-            else:
-                word_pairs.append((word, adjusted_start))
-
-        return word_pairs
-
-    async def flush_audio(self):
-        if not self._context_id or not self._websocket:
-            return
-        logger.trace(f"{self}: flushing audio")
-        self._context_id = None
-
-    async def _receive_messages(self):
-        """Process incoming websocket messages."""
-        async for message in self._get_websocket():
-            msg = json.loads(message)
-
-            if not msg or not self.audio_context_available(msg["contextId"]):
-                continue
-
-            if msg["type"] == "chunk":
-                # Process audio chunk
-                await self.stop_ttfb_metrics()
-                self.start_word_timestamps()
-                frame = TTSAudioRawFrame(
-                    audio=base64.b64decode(msg["data"]),
-                    sample_rate=self.sample_rate,
-                    num_channels=1,
-                )
-                await self.append_to_audio_context(msg["contextId"], frame)
-
-            elif msg["type"] == "timestamps":
-                # Process word timing information
-                timestamps = msg.get("word_timestamps", {})
-                words = timestamps.get("words", [])
-                starts = timestamps.get("start", [])
-                ends = timestamps.get("end", [])
-
-                if words and starts:
-                    # Calculate word timing pairs
-                    word_pairs = self._calculate_word_times(words, starts, ends)
-                    if word_pairs:
-                        await self.add_word_timestamps(word_pairs)
-                        self._cumulative_time = ends[-1] + self._cumulative_time
-                        logger.debug(f"Updated cumulative time to: {self._cumulative_time}")
-
-            elif msg["type"] == "error":
-                logger.error(f"{self} error: {msg}")
-                await self.push_frame(TTSStoppedFrame())
-                await self.stop_all_metrics()
-                await self.push_error(ErrorFrame(f"{self} error: {msg['message']}"))
-                self._context_id = None
-
-    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
-        """Push frame and handle end-of-turn conditions."""
-        await super().push_frame(frame, direction)
-        if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
-            if isinstance(frame, TTSStoppedFrame):
-                await self.add_word_timestamps([("LLMFullResponseEndFrame", 0), ("Reset", 0)])
-
-    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text.
-
-        Args:
-            text: The text to convert to speech.
-
-        Yields:
-            Frames containing audio data and timing information.
-        """
-        logger.debug(f"Generating TTS: [{text}]")
-        try:
-            if not self._websocket:
-                await self._connect()
-
-            try:
-                if not self._context_id:
-                    await self.start_ttfb_metrics()
-                    yield TTSStartedFrame()
-                    self._cumulative_time = 0
-                    self._context_id = str(uuid.uuid4())
-                    await self.create_audio_context(self._context_id)
-
-                msg = self._build_msg(text=text)
-                await self._get_websocket().send(json.dumps(msg))
-                await self.start_tts_usage_metrics(text)
-            except Exception as e:
-                logger.error(f"{self} error sending message: {e}")
-                yield TTSStoppedFrame()
-                await self._disconnect()
-                await self._connect()
-                return
-            yield None
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
+from pipecat.services.ai_services import TTSService


 class RimeHttpTTSService(TTSService):
@@ -345,9 +32,8 @@ class RimeHttpTTSService(TTSService):
        self,
        *,
        api_key: str,
-        voice_id: str,
-        aiohttp_session: aiohttp.ClientSession,
-        model: str = "mistv2",
+        voice_id: str = "eva",
+        model: str = "mist",
        sample_rate: Optional[int] = None,
        params: InputParams = InputParams(),
        **kwargs,
@@ -355,7 +41,6 @@ class RimeHttpTTSService(TTSService):
        super().__init__(sample_rate=sample_rate, **kwargs)

        self._api_key = api_key
-        self._session = aiohttp_session
        self._base_url = "https://users.rime.ai/v1/rime-tts"
        self._settings = {
            "speedAlpha": params.speed_alpha,
@@ -389,31 +74,36 @@ class RimeHttpTTSService(TTSService):

        try:
            await self.start_ttfb_metrics()
+            await self.start_tts_usage_metrics(text)

-            async with self._session.post(
-                self._base_url, json=payload, headers=headers
-            ) as response:
-                if response.status != 200:
-                    error_message = f"Rime TTS error: HTTP {response.status}"
-                    logger.error(error_message)
-                    yield ErrorFrame(error=error_message)
-                    return
+            yield TTSStartedFrame()

-                await self.start_tts_usage_metrics(text)
+            async with aiohttp.ClientSession() as session:
+                async with session.post(self._base_url, json=payload, headers=headers) as response:
+                    if response.status != 200:
+                        error_message = f"Rime TTS error: HTTP {response.status}"
+                        logger.error(error_message)
+                        yield ErrorFrame(error=error_message)
+                        return

-                yield TTSStartedFrame()
+                    # Process the streaming response
+                    chunk_size = 8192
+                    first_chunk = True

-                # Process the streaming response
-                chunk_size = 8192
+                    async for chunk in response.content.iter_chunked(chunk_size):
+                        if first_chunk:
+                            await self.stop_ttfb_metrics()
+                            first_chunk = False
+
+                        if chunk:
+                            frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
+                            yield frame
+
+            yield TTSStoppedFrame()

-                async for chunk in response.content.iter_chunked(chunk_size):
-                    if chunk:
-                        await self.stop_ttfb_metrics()
-                        frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
-                        yield frame
        except Exception as e:
            logger.exception(f"Error generating TTS: {e}")
            yield ErrorFrame(error=f"Rime TTS error: {str(e)}")
+
        finally:
-            await self.stop_ttfb_metrics()
            yield TTSStoppedFrame()
--- a/src/pipecat/services/simli.py
+++ b/src/pipecat/services/simli.py
@@ -41,7 +41,7 @@ class SimliVideoService(FrameProcessor):

        self._pipecat_resampler_event = asyncio.Event()
        self._pipecat_resampler: AudioResampler = None
-        self._simli_resampler = AudioResampler("s16", "mono", 16000)
+        self._simli_resampler = AudioResampler("s16", 1, 16000)

        self._audio_task: asyncio.Task = None
        self._video_task: asyncio.Task = None
--- a/src/pipecat/services/websocket_service.py
+++ b/src/pipecat/services/websocket_service.py
@@ -10,10 +10,8 @@ from typing import Awaitable, Callable, Optional

 import websockets
 from loguru import logger
-from websockets.protocol import State

 from pipecat.frames.frames import ErrorFrame
-from pipecat.utils.network import exponential_backoff_time


 class WebsocketService(ABC):
@@ -52,6 +50,27 @@ class WebsocketService(ABC):
        await self._connect_websocket()
        return await self._verify_connection()

+    def _calculate_wait_time(
+        self, attempt: int, min_wait: float = 4, max_wait: float = 10, multiplier: float = 1
+    ) -> float:
+        """Calculate exponential backoff wait time.
+
+        Args:
+            attempt: Current attempt number (1-based)
+            min_wait: Minimum wait time in seconds
+            max_wait: Maximum wait time in seconds
+            multiplier: Base multiplier for exponential calculation
+
+        Returns:
+            Wait time in seconds
+        """
+        try:
+            exp = 2 ** (attempt - 1) * multiplier
+            result = max(0, min(exp, max_wait))
+            return max(min_wait, result)
+        except (ValueError, ArithmeticError):
+            return max_wait
+
    async def _receive_task_handler(self, report_error: Callable[[ErrorFrame], Awaitable[None]]):
        """Handles WebSocket message receiving with automatic retry logic.

@@ -64,13 +83,11 @@ class WebsocketService(ABC):
        while True:
            try:
                await self._receive_messages()
+                logger.debug(f"{self} connection established successfully")
                retry_count = 0  # Reset counter on successful message receive
-                if self._websocket and self._websocket.state == State.CLOSED:
-                    raise websockets.ConnectionClosedOK(
-                        self._websocket.close_rcvd,
-                        self._websocket.close_sent,
-                        self._websocket.close_rcvd_then_sent,
-                    )
+            except websockets.ConnectionClosed as e:
+                logger.warning(f"{self} connection closed: {e}")
+                break
            except Exception as e:
                retry_count += 1
                if retry_count >= MAX_RETRIES:
@@ -84,7 +101,7 @@ class WebsocketService(ABC):
                try:
                    if await self._reconnect_websocket(retry_count):
                        retry_count = 0  # Reset counter on successful reconnection
-                    wait_time = exponential_backoff_time(retry_count)
+                    wait_time = self._calculate_wait_time(retry_count)
                    await asyncio.sleep(wait_time)
                except Exception as reconnect_error:
                    logger.error(f"{self} reconnection failed: {reconnect_error}")
--- a/src/pipecat/tests/utils.py
+++ b/src/pipecat/tests/utils.py
@@ -5,7 +5,6 @@
 #

 import asyncio
-from dataclasses import dataclass
 from typing import Any, Awaitable, Callable, Dict, Sequence, Tuple

 from pipecat.frames.frames import (
@@ -13,7 +12,6 @@ from pipecat.frames.frames import (
    Frame,
    HeartbeatFrame,
    StartFrame,
-    SystemFrame,
 )
 from pipecat.observers.base_observer import BaseObserver
 from pipecat.pipeline.pipeline import Pipeline
@@ -22,16 +20,6 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor


-@dataclass
-class SleepFrame(SystemFrame):
-    """This frame is used by test framework to introduce some sleep time before
-    the next frame is pushed. This is useful to control system frames vs data or
-    control frames.
-    """
-
-    sleep: float = 0.1
-
-
 class HeartbeatsObserver(BaseObserver):
    def __init__(
        self,
@@ -56,11 +44,7 @@ class HeartbeatsObserver(BaseObserver):

 class QueuedFrameProcessor(FrameProcessor):
    def __init__(
-        self,
-        *,
-        queue: asyncio.Queue,
-        queue_direction: FrameDirection,
-        ignore_start: bool = True,
+        self, queue: asyncio.Queue, queue_direction: FrameDirection, ignore_start: bool = True
    ):
        super().__init__()
        self._queue = queue
@@ -88,35 +72,21 @@ async def run_test(
 ) -> Tuple[Sequence[Frame], Sequence[Frame]]:
    received_up = asyncio.Queue()
    received_down = asyncio.Queue()
-    source = QueuedFrameProcessor(
-        queue=received_up,
-        queue_direction=FrameDirection.UPSTREAM,
-        ignore_start=ignore_start,
-    )
-    sink = QueuedFrameProcessor(
-        queue=received_down,
-        queue_direction=FrameDirection.DOWNSTREAM,
-        ignore_start=ignore_start,
-    )
+    source = QueuedFrameProcessor(received_up, FrameDirection.UPSTREAM, ignore_start)
+    sink = QueuedFrameProcessor(received_down, FrameDirection.DOWNSTREAM, ignore_start)

    pipeline = Pipeline([source, processor, sink])

    task = PipelineTask(pipeline, params=PipelineParams(start_metadata=start_metadata))

-    async def push_frames():
-        # Just give a little head start to the runner.
-        await asyncio.sleep(0.01)
-        for frame in frames_to_send:
-            if isinstance(frame, SleepFrame):
-                await asyncio.sleep(frame.sleep)
-            else:
-                await task.queue_frame(frame)
+    for frame in frames_to_send:
+        await task.queue_frame(frame)

-        if send_end_frame:
-            await task.queue_frame(EndFrame())
+    if send_end_frame:
+        await task.queue_frame(EndFrame())

    runner = PipelineRunner()
-    await asyncio.gather(runner.run(task), push_frames())
+    await runner.run(task)

    #
    # Down frames
@@ -128,7 +98,6 @@ async def run_test(
            received_down_frames.append(frame)

    print("received DOWN frames =", received_down_frames)
-    print("expected DOWN frames =", expected_down_frames)

    assert len(received_down_frames) == len(expected_down_frames)

@@ -144,7 +113,6 @@ async def run_test(
        received_up_frames.append(frame)

    print("received UP frames =", received_up_frames)
-    print("expected UP frames =", expected_up_frames)

    assert len(received_up_frames) == len(expected_up_frames)

--- a/src/pipecat/transcriptions/language.py
+++ b/src/pipecat/transcriptions/language.py
@@ -101,7 +101,6 @@ class Language(StrEnum):
    EN_AU = "en-AU"
    EN_CA = "en-CA"
    EN_GB = "en-GB"
-    EN_GH = "en-GH"
    EN_HK = "en-HK"
    EN_IE = "en-IE"
    EN_IN = "en-IN"
@@ -209,7 +208,6 @@ class Language(StrEnum):
    # Italian
    IT = "it"
    IT_IT = "it-IT"
-    IT_CH = "it-CH"

    # Inuktitut
    IU_CANS = "iu-Cans"
--- a/src/pipecat/transports/base_input.py
+++ b/src/pipecat/transports/base_input.py
@@ -14,8 +14,6 @@ from pipecat.audio.vad.vad_analyzer import VADAnalyzer, VADState
 from pipecat.frames.frames import (
    BotInterruptionFrame,
    CancelFrame,
-    EmulateUserStartedSpeakingFrame,
-    EmulateUserStoppedSpeakingFrame,
    EndFrame,
    FilterUpdateSettingsFrame,
    Frame,
@@ -49,13 +47,6 @@ class BaseInputTransport(FrameProcessor):
        # if passthrough is enabled.
        self._audio_task = None

-    def enable_audio_in_stream_on_start(self, enabled: bool) -> None:
-        logger.debug(f"Enabling audio on start. {enabled}")
-        self._params.audio_in_stream_on_start = enabled
-
-    def start_audio_in_streaming(self):
-        pass
-
    @property
    def sample_rate(self) -> int:
        return self._sample_rate
@@ -114,13 +105,9 @@ class BaseInputTransport(FrameProcessor):
            await self.cancel(frame)
            await self.push_frame(frame, direction)
        elif isinstance(frame, BotInterruptionFrame):
-            await self._handle_bot_interruption(frame)
-        elif isinstance(frame, EmulateUserStartedSpeakingFrame):
-            logger.debug("Emulating user started speaking")
-            await self._handle_user_interruption(UserStartedSpeakingFrame())
-        elif isinstance(frame, EmulateUserStoppedSpeakingFrame):
-            logger.debug("Emulating user stopped speaking")
-            await self._handle_user_interruption(UserStoppedSpeakingFrame())
+            logger.debug("Bot interruption")
+            await self._start_interruption()
+            await self.push_frame(StartInterruptionFrame())
        # All other system frames
        elif isinstance(frame, SystemFrame):
            await self.push_frame(frame, direction)
@@ -143,13 +130,7 @@ class BaseInputTransport(FrameProcessor):
    # Handle interruptions
    #

-    async def _handle_bot_interruption(self, frame: BotInterruptionFrame):
-        logger.debug("Bot interruption")
-        if self.interruptions_allowed:
-            await self._start_interruption()
-            await self.push_frame(StartInterruptionFrame())
-
-    async def _handle_user_interruption(self, frame: Frame):
+    async def _handle_interruptions(self, frame: Frame):
        if isinstance(frame, UserStartedSpeakingFrame):
            logger.debug("User started speaking")
            # Make sure we notify about interruptions quickly out-of-band.
@@ -174,9 +155,11 @@ class BaseInputTransport(FrameProcessor):
    async def _vad_analyze(self, audio_frame: InputAudioRawFrame) -> VADState:
        state = VADState.QUIET
        if self.vad_analyzer:
+            logger.trace(f"{self}: analyzing VAD on {audio_frame}")
            state = await self.get_event_loop().run_in_executor(
                self._executor, self.vad_analyzer.analyze_audio, audio_frame.audio
            )
+            logger.trace(f"{self}: done analyzing VAD on {audio_frame}")
        return state

    async def _handle_vad(self, audio_frame: InputAudioRawFrame, vad_state: VADState):
@@ -193,7 +176,7 @@ class BaseInputTransport(FrameProcessor):
                frame = UserStoppedSpeakingFrame()

            if frame:
-                await self._handle_user_interruption(frame)
+                await self._handle_interruptions(frame)

            vad_state = new_vad_state
        return vad_state
--- a/src/pipecat/transports/base_output.py
+++ b/src/pipecat/transports/base_output.py
@@ -14,6 +14,7 @@ from loguru import logger
 from PIL import Image

 from pipecat.audio.utils import create_default_resampler
+from pipecat.audio.vad.vad_analyzer import VAD_STOP_SECS
 from pipecat.frames.frames import (
    BotSpeakingFrame,
    BotStartedSpeakingFrame,
@@ -37,8 +38,6 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transports.base_transport import TransportParams
 from pipecat.utils.time import nanoseconds_to_seconds

-BOT_VAD_STOP_SECS = 0.3
-

 class BaseOutputTransport(FrameProcessor):
    def __init__(self, params: TransportParams, **kwargs):
@@ -170,8 +169,6 @@ class BaseOutputTransport(FrameProcessor):
        # TODO(aleix): Images and audio should support presentation timestamps.
        elif frame.pts:
            await self._sink_clock_queue.put((frame.pts, frame.id, frame))
-        elif direction == FrameDirection.UPSTREAM:
-            await self.push_frame(frame, direction)
        else:
            await self._sink_queue.put(frame)

@@ -324,10 +321,15 @@ class BaseOutputTransport(FrameProcessor):
                    )
                    yield frame

+        vad_stop_secs = (
+            self._params.vad_analyzer.params.stop_secs
+            if self._params.vad_analyzer
+            else VAD_STOP_SECS
+        )
        if self._params.audio_out_mixer:
-            return with_mixer(BOT_VAD_STOP_SECS)
+            return with_mixer(vad_stop_secs)
        else:
-            return without_mixer(BOT_VAD_STOP_SECS)
+            return without_mixer(vad_stop_secs)

    async def _sink_task_handler(self):
        async for frame in self._next_frame():
--- a/src/pipecat/transports/base_transport.py
+++ b/src/pipecat/transports/base_transport.py
@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

+import asyncio
 import inspect
 from abc import ABC, abstractmethod
 from typing import Optional
@@ -29,6 +30,7 @@ class TransportParams(BaseModel):
    camera_out_framerate: int = 30
    camera_out_color_format: str = "RGB"
    audio_out_enabled: bool = False
+    audio_out_is_live: bool = False
    audio_out_sample_rate: Optional[int] = None
    audio_out_channels: int = 1
    audio_out_bitrate: int = 96000
@@ -37,7 +39,6 @@ class TransportParams(BaseModel):
    audio_in_sample_rate: Optional[int] = None
    audio_in_channels: int = 1
    audio_in_filter: Optional[BaseAudioFilter] = None
-    audio_in_stream_on_start: bool = True
    vad_enabled: bool = False
    vad_audio_passthrough: bool = False
    vad_analyzer: Optional[VADAnalyzer] = None
--- a/src/pipecat/transports/local/audio.py
+++ b/src/pipecat/transports/local/audio.py
@@ -26,18 +26,17 @@ except ModuleNotFoundError as e:
    raise Exception(f"Missing module: {e}")


-class LocalAudioTransportParams(TransportParams):
-    input_device_index: Optional[int] = None
-    output_device_index: Optional[int] = None
+class LocalTransportParams(TransportParams):
+    input_device_index: int = 0
+    output_device_index: int = 0


 class LocalAudioInputTransport(BaseInputTransport):
-    _params: LocalAudioTransportParams
+    _params: LocalTransportParams

-    def __init__(self, py_audio: pyaudio.PyAudio, params: LocalAudioTransportParams):
+    def __init__(self, py_audio: pyaudio.PyAudio, params: LocalTransportParams):
        super().__init__(params)
        self._py_audio = py_audio
-
        self._in_stream = None
        self._sample_rate = 0

@@ -78,12 +77,11 @@ class LocalAudioInputTransport(BaseInputTransport):


 class LocalAudioOutputTransport(BaseOutputTransport):
-    _params: LocalAudioTransportParams
+    _params: LocalTransportParams

-    def __init__(self, py_audio: pyaudio.PyAudio, params: LocalAudioTransportParams):
+    def __init__(self, py_audio: pyaudio.PyAudio, params: TransportParams):
        super().__init__(params)
        self._py_audio = py_audio
-
        self._out_stream = None
        self._sample_rate = 0

@@ -119,7 +117,7 @@ class LocalAudioOutputTransport(BaseOutputTransport):


 class LocalAudioTransport(BaseTransport):
-    def __init__(self, params: LocalAudioTransportParams):
+    def __init__(self, params: LocalTransportParams):
        super().__init__()
        self._params = params
        self._pyaudio = pyaudio.PyAudio()
--- a/src/pipecat/transports/local/tk.py
+++ b/src/pipecat/transports/local/tk.py
@@ -34,15 +34,8 @@ except ModuleNotFoundError as e:
    raise Exception(f"Missing module: {e}")


-class TkTransportParams(TransportParams):
-    audio_input_device_index: Optional[int] = None
-    audio_output_device_index: Optional[int] = None
-
-
 class TkInputTransport(BaseInputTransport):
-    _params: TkTransportParams
-
-    def __init__(self, py_audio: pyaudio.PyAudio, params: TkTransportParams):
+    def __init__(self, py_audio: pyaudio.PyAudio, params: TransportParams):
        super().__init__(params)
        self._py_audio = py_audio
        self._in_stream = None
@@ -61,7 +54,6 @@ class TkInputTransport(BaseInputTransport):
            frames_per_buffer=num_frames,
            stream_callback=self._audio_in_callback,
            input=True,
-            input_device_index=self._params.audio_input_device_index,
        )
        self._in_stream.start_stream()

@@ -84,8 +76,6 @@ class TkInputTransport(BaseInputTransport):


 class TkOutputTransport(BaseOutputTransport):
-    _params: TkTransportParams
-
    def __init__(self, tk_root: tk.Tk, py_audio: pyaudio.PyAudio, params: TransportParams):
        super().__init__(params)
        self._py_audio = py_audio
@@ -113,7 +103,6 @@ class TkOutputTransport(BaseOutputTransport):
            channels=self._params.audio_out_channels,
            rate=self._sample_rate,
            output=True,
-            output_device_index=self._params.audio_output_device_index,
        )
        self._out_stream.start_stream()

--- a/src/pipecat/transports/network/fastapi_websocket.py
+++ b/src/pipecat/transports/network/fastapi_websocket.py
@@ -23,8 +23,6 @@ from pipecat.frames.frames import (
    OutputAudioRawFrame,
    StartFrame,
    StartInterruptionFrame,
-    TransportMessageFrame,
-    TransportMessageUrgentFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
@@ -55,89 +53,45 @@ class FastAPIWebsocketCallbacks(BaseModel):
    on_session_timeout: Callable[[WebSocket], Awaitable[None]]


-class FastAPIWebsocketClient:
-    def __init__(self, websocket: WebSocket, is_binary: bool, callbacks: FastAPIWebsocketCallbacks):
-        self._websocket = websocket
-        self._closing = False
-        self._is_binary = is_binary
-        self._callbacks = callbacks
-
-    def receive(self) -> typing.AsyncIterator[bytes | str]:
-        return self._websocket.iter_bytes() if self._is_binary else self._websocket.iter_text()
-
-    async def send(self, data: str | bytes):
-        if self._can_send():
-            if self._is_binary:
-                await self._websocket.send_bytes(data)
-            else:
-                await self._websocket.send_text(data)
-
-    async def disconnect(self):
-        if self.is_connected and not self.is_closing:
-            self._closing = True
-            await self._websocket.close()
-            await self.trigger_client_disconnected()
-
-    async def trigger_client_disconnected(self):
-        await self._callbacks.on_client_disconnected(self._websocket)
-
-    async def trigger_client_connected(self):
-        await self._callbacks.on_client_connected(self._websocket)
-
-    async def trigger_client_timout(self):
-        await self._callbacks.on_session_timeout(self._websocket)
-
-    def _can_send(self):
-        return self.is_connected and not self.is_closing
-
-    @property
-    def is_connected(self) -> bool:
-        return self._websocket.client_state == WebSocketState.CONNECTED
-
-    @property
-    def is_closing(self) -> bool:
-        return self._closing
-
-
 class FastAPIWebsocketInputTransport(BaseInputTransport):
    def __init__(
        self,
-        client: FastAPIWebsocketClient,
+        websocket: WebSocket,
        params: FastAPIWebsocketParams,
+        callbacks: FastAPIWebsocketCallbacks,
        **kwargs,
    ):
        super().__init__(params, **kwargs)
-        self._client = client
+
+        self._websocket = websocket
        self._params = params
-        self._receive_task = None
-        self._monitor_websocket_task = None
+        self._callbacks = callbacks

    async def start(self, frame: StartFrame):
        await super().start(frame)
        await self._params.serializer.setup(frame)
        if self._params.session_timeout:
            self._monitor_websocket_task = self.create_task(self._monitor_websocket())
-        await self._client.trigger_client_connected()
+        await self._callbacks.on_client_connected(self._websocket)
        self._receive_task = self.create_task(self._receive_messages())

-    async def _stop_tasks(self):
-        if self._monitor_websocket_task:
-            await self.cancel_task(self._monitor_websocket_task)
-        await self.cancel_task(self._receive_task)
-
    async def stop(self, frame: EndFrame):
        await super().stop(frame)
-        await self._stop_tasks()
-        await self._client.disconnect()
+        await self.cancel_task(self._receive_task)

    async def cancel(self, frame: CancelFrame):
        await super().cancel(frame)
-        await self._stop_tasks()
-        await self._client.disconnect()
+        await self.cancel_task(self._receive_task)
+
+    def _iter_data(self) -> typing.AsyncIterator[bytes | str]:
+        if self._params.serializer.type == FrameSerializerType.BINARY:
+            return self._websocket.iter_bytes()
+        else:
+            return self._websocket.iter_text()

    async def _receive_messages(self):
        try:
-            async for message in self._client.receive():
+            async for message in self._iter_data():
                frame = await self._params.serializer.deserialize(message)

                if not frame:
@@ -150,23 +104,19 @@ class FastAPIWebsocketInputTransport(BaseInputTransport):
        except Exception as e:
            logger.error(f"{self} exception receiving data: {e.__class__.__name__} ({e})")

-        await self._client.trigger_client_disconnected()
+        await self._callbacks.on_client_disconnected(self._websocket)

    async def _monitor_websocket(self):
        """Wait for self._params.session_timeout seconds, if the websocket is still open, trigger timeout event."""
        await asyncio.sleep(self._params.session_timeout)
-        await self._client.trigger_client_timout()
+        await self._callbacks.on_session_timeout(self._websocket)


 class FastAPIWebsocketOutputTransport(BaseOutputTransport):
-    def __init__(
-        self,
-        client: FastAPIWebsocketClient,
-        params: FastAPIWebsocketParams,
-        **kwargs,
-    ):
+    def __init__(self, websocket: WebSocket, params: FastAPIWebsocketParams, **kwargs):
        super().__init__(params, **kwargs)
-        self._client = client
+
+        self._websocket = websocket
        self._params = params

        # write_raw_audio_frames() is called quickly, as soon as we get audio
@@ -182,14 +132,6 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):
        await self._params.serializer.setup(frame)
        self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2

-    async def stop(self, frame: EndFrame):
-        await super().stop(frame)
-        await self._client.disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        await super().cancel(frame)
-        await self._client.disconnect()
-
    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

@@ -197,14 +139,8 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):
            await self._write_frame(frame)
            self._next_send_time = 0

-    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
-        await self._write_frame(frame)
-
    async def write_raw_audio_frames(self, frames: bytes):
-        if self._client.is_closing:
-            return
-
-        if not self._client.is_connected:
+        if self._websocket.client_state != WebSocketState.CONNECTED:
            # Simulate audio playback with a sleep.
            await self._write_audio_sleep()
            return
@@ -231,17 +167,25 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):

        await self._write_frame(frame)

+        self._websocket_audio_buffer = bytes()
+
        # Simulate audio playback with a sleep.
        await self._write_audio_sleep()

    async def _write_frame(self, frame: Frame):
        try:
            payload = await self._params.serializer.serialize(frame)
-            if payload:
-                await self._client.send(payload)
+            if payload and self._websocket.client_state == WebSocketState.CONNECTED:
+                await self._send_data(payload)
        except Exception as e:
            logger.error(f"{self} exception sending data: {e.__class__.__name__} ({e})")

+    def _send_data(self, data: str | bytes):
+        if self._params.serializer.type == FrameSerializerType.BINARY:
+            return self._websocket.send_bytes(data)
+        else:
+            return self._websocket.send_text(data)
+
    async def _write_audio_sleep(self):
        # Simulate a clock.
        current_time = time.monotonic()
@@ -270,14 +214,11 @@ class FastAPIWebsocketTransport(BaseTransport):
            on_session_timeout=self._on_session_timeout,
        )

-        is_binary = self._params.serializer.type == FrameSerializerType.BINARY
-        self._client = FastAPIWebsocketClient(websocket, is_binary, self._callbacks)
-
        self._input = FastAPIWebsocketInputTransport(
-            self._client, self._params, name=self._input_name
+            websocket, self._params, self._callbacks, name=self._input_name
        )
        self._output = FastAPIWebsocketOutputTransport(
-            self._client, self._params, name=self._output_name
+            websocket, self._params, name=self._output_name
        )

        # Register supported handlers. The user will only be able to register
--- a/src/pipecat/transports/network/websocket_server.py
+++ b/src/pipecat/transports/network/websocket_server.py
@@ -6,7 +6,6 @@

 import asyncio
 import io
-import json
 import time
 import wave
 from typing import Awaitable, Callable, Optional
@@ -22,9 +21,6 @@ from pipecat.frames.frames import (
    OutputAudioRawFrame,
    StartFrame,
    StartInterruptionFrame,
-    TextFrame,
-    TransportMessageFrame,
-    TransportMessageUrgentFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.serializers.base_serializer import FrameSerializer
@@ -50,7 +46,6 @@ class WebsocketServerCallbacks(BaseModel):
    on_client_connected: Callable[[websockets.WebSocketServerProtocol], Awaitable[None]]
    on_client_disconnected: Callable[[websockets.WebSocketServerProtocol], Awaitable[None]]
    on_session_timeout: Callable[[websockets.WebSocketServerProtocol], Awaitable[None]]
-    on_websocket_ready: Callable[[], Awaitable[None]]


 class WebsocketServerInputTransport(BaseInputTransport):
@@ -101,7 +96,6 @@ class WebsocketServerInputTransport(BaseInputTransport):
    async def _server_task_handler(self):
        logger.info(f"Starting websocket server on {self._host}:{self._port}")
        async with websockets.serve(self._client_handler, self._host, self._port) as server:
-            await self._callbacks.on_websocket_ready()
            await self._stop_server_event.wait()

    async def _client_handler(self, websocket: websockets.WebSocketServerProtocol, path):
@@ -192,9 +186,6 @@ class WebsocketServerOutputTransport(BaseOutputTransport):
            await self._write_frame(frame)
            self._next_send_time = 0

-    async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
-        await self._write_frame(frame)
-
    async def write_raw_audio_frames(self, frames: bytes):
        if not self._websocket:
            # Simulate audio playback with a sleep.
@@ -263,7 +254,6 @@ class WebsocketServerTransport(BaseTransport):
            on_client_connected=self._on_client_connected,
            on_client_disconnected=self._on_client_disconnected,
            on_session_timeout=self._on_session_timeout,
-            on_websocket_ready=self._on_websocket_ready,
        )
        self._input: Optional[WebsocketServerInputTransport] = None
        self._output: Optional[WebsocketServerOutputTransport] = None
@@ -274,7 +264,6 @@ class WebsocketServerTransport(BaseTransport):
        self._register_event_handler("on_client_connected")
        self._register_event_handler("on_client_disconnected")
        self._register_event_handler("on_session_timeout")
-        self._register_event_handler("on_websocket_ready")

    def input(self) -> WebsocketServerInputTransport:
        if not self._input:
@@ -304,6 +293,3 @@ class WebsocketServerTransport(BaseTransport):

    async def _on_session_timeout(self, websocket):
        await self._call_event_handler("on_session_timeout", websocket)
-
-    async def _on_websocket_ready(self):
-        await self._call_event_handler("on_websocket_ready")
--- a/src/pipecat/transports/services/daily.py
+++ b/src/pipecat/transports/services/daily.py
@@ -13,6 +13,9 @@ from typing import Any, Awaitable, Callable, Mapping, Optional

 import aiohttp
 from daily import (
+    CallClient,
+    Daily,
+    EventHandler,
    VirtualCameraDevice,
    VirtualMicrophoneDevice,
    VirtualSpeakerDevice,
@@ -288,7 +291,6 @@ class DailyTransportClient(EventHandler):
        self._transcription_ids = []
        self._transcription_status = None

-        self._joining = False
        self._joined = False
        self._joined_event = asyncio.Event()
        self._leave_counter = 0
@@ -329,10 +331,6 @@ class DailyTransportClient(EventHandler):
    def _speaker_name(self):
        return f"speaker-{self}"

-    @property
-    def room_url(self) -> str:
-        return self._room_url
-
    @property
    def participant_id(self) -> str:
        return self._participant_id
@@ -425,14 +423,13 @@ class DailyTransportClient(EventHandler):
            )

    async def join(self):
-        # Transport already joined or joining, ignore.
-        if self._joined or self._joining:
+        # Transport already joined, ignore.
+        if self._joined:
            # Increment leave counter if we already joined.
            self._leave_counter += 1
            return

        logger.info(f"Joining {self._room_url}")
-        self._joining = True

        # For performance reasons, never subscribe to video streams (unless a
        # video renderer is registered).
@@ -447,7 +444,6 @@ class DailyTransportClient(EventHandler):

            if not error:
                self._joined = True
-                self._joining = False
                # Increment leave counter if we successfully joined.
                self._leave_counter += 1

@@ -466,7 +462,6 @@ class DailyTransportClient(EventHandler):
        except asyncio.TimeoutError:
            error_msg = f"Time out joining {self._room_url}"
            logger.error(error_msg)
-            self._joining = False
            await self._callbacks.on_error(error_msg)

    async def _start_transcription(self):
@@ -842,13 +837,6 @@ class DailyInputTransport(BaseInputTransport):
    def vad_analyzer(self) -> Optional[VADAnalyzer]:
        return self._vad_analyzer

-    def start_audio_in_streaming(self):
-        # Create audio task. It reads audio frames from Daily and push them
-        # internally for VAD processing.
-        if self._params.audio_in_enabled or self._params.vad_enabled:
-            logger.debug(f"Start receiving audio")
-            self._audio_in_task = self.create_task(self._audio_in_task_handler())
-
    async def start(self, frame: StartFrame):
        # Parent start.
        await super().start(frame)
@@ -859,8 +847,10 @@ class DailyInputTransport(BaseInputTransport):
        # Inialize WebRTC VAD if needed.
        if self._params.vad_enabled and not self._params.vad_analyzer:
            self._vad_analyzer = WebRTCVADAnalyzer(sample_rate=self.sample_rate)
-        if self._params.audio_in_stream_on_start:
-            self.start_audio_in_streaming()
+        # Create audio task. It reads audio frames from Daily and push them
+        # internally for VAD processing.
+        if self._params.audio_in_enabled or self._params.vad_enabled:
+            self._audio_in_task = self.create_task(self._audio_in_task_handler())

    async def stop(self, frame: EndFrame):
        # Parent stop.
@@ -1116,10 +1106,6 @@ class DailyTransport(BaseTransport):
    # DailyTransport
    #

-    @property
-    def room_url(self) -> str:
-        return self._client.room_url
-
    @property
    def participant_id(self) -> str:
        return self._client.participant_id
--- a/src/pipecat/utils/network.py
+++ b/src/pipecat/utils/network.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-def exponential_backoff_time(
-    attempt: int, min_wait: float = 4, max_wait: float = 10, multiplier: float = 1
-) -> float:
-    """Calculate exponential backoff wait time.
-
-    Args:
-        attempt: Current attempt number (1-based)
-        min_wait: Minimum wait time in seconds
-        max_wait: Maximum wait time in seconds
-        multiplier: Base multiplier for exponential calculation
-
-    Returns:
-        Wait time in seconds
-    """
-    try:
-        exp = 2 ** (attempt - 1) * multiplier
-        result = max(0, min(exp, max_wait))
-        return max(min_wait, result)
-    except (ValueError, ArithmeticError):
-        return max_wait
--- a/Show More
+++ b/Show More