Replace hello with banana

Mentioning the Smart Turn Detection into the changelog.

CHANGELOG.md

@@ -5,6 +5,72 @@ 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]
+
+### Added
+
+- Added support for Smart Turn Detection via the `turn_analyzer` transport
+  parameter. You can now choose between `SmartTurnAnalyzer()` for remote 
+  inference or `LocalCoreMLSmartTurnAnalyzer()` for on-device inference using 
+  Core ML.
+
+- `DeepgramTTSService` accepts `base_url` argument again, allowing you to
+  connect to an on-prem service.
+
+- Added `LLMUserAggregatorParams` and `LLMAssistantAggregatorParams` which allow
+  you to control aggregator settings. You can now pass these arguments when
+  creating aggregator pairs with `create_context_aggregator()`.
+
+- Added `previous_text` context support to ElevenLabsHttpTTSService, improving
+  speech consistency across sentences within an LLM response.
+
+- Added word/timestamp pairs to `ElevenLabsHttpTTSService`.
+
+- It is now possible to disable `SoundfileMixer` when created. You can then use
+  `MixerEnableFrame` to dynamically enable it when necessary.
+
+- Added `on_client_connected` and `on_client_disconnected` event handlers to
+  the `DailyTransport` class. These handlers map to the same underlying Daily
+  events as `on_participant_joined` and `on_participant_left`, respectively.
+  This makes it easier to write a single bot pipeline that can also use other
+  transports like `SmallWebRTCTransport` and `FastAPIWebsocketTransport`.
+
+### Changed
+
+- Daily's REST helpers now include an `eject_at_token_exp` param, which ejects
+  the user when their token expires. This new parameter defaults to False.
+  Also, the default value for `enable_prejoin_ui` changed to False and
+  `eject_at_room_exp` changed to False.
+
+- `OpenAILLMService` and `OpenPipeLLMService` now use `gpt-4.1` as their
+  default model.
+
+- `SoundfileMixer` constructor arguments need to be keywords.
+
+### Deprecated
+
+- `DeepgramSTTService` parameter `url` is now deprecated, use `base_url`
+  instead.
+
+### Removed
+
+- Parameters `user_kwargs` and `assistant_kwargs` when creating a context
+  aggregator pair using `create_context_aggregator()` have been removed. Use
+  `user_params` and `assistant_params` instead.
+
+### Fixed
+
+- Fixed an issue that would cause TTS websocket-based services to not cleanup
+  resources properly when disconnecting.
+
+- Fixed a `TavusVideoService` issue that was causing audio choppiness.
+
+- Fixed an issue in `SmallWebRTCTransport` where an error was thrown if the
+  client did not create a video transceiver.
+
+- Fixed an issue where LLM input parameters were not working and applied correctly in `GoogleVertexLLMService`, causing
+  unexpected behavior during inference.
+
 ## [0.0.63] - 2025-04-11
 
 ### Added

dot-env.template

@@ -92,4 +92,8 @@ ASSEMBLYAI_API_KEY=...
 OPENROUTER_API_KEY=...
 
 # Piper
-PIPER_BASE_URL=...
+PIPER_BASE_URL=...
+
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=
+REMOTE_SMART_TURN_URL=

examples/canonical-metrics/bot.py

@@ -72,7 +72,7 @@ async def main():
             # voice_id="gD1IexrzCvsXPHUuT0s3",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

examples/chatbot-audio-recording/bot.py

@@ -95,7 +95,7 @@ async def main():
             # voice_id="gD1IexrzCvsXPHUuT0s3",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

examples/deployment/flyio-example/bot.py

@@ -53,7 +53,7 @@ async def main(room_url: str, token: str):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/deployment/modal-example/bot.py

@@ -43,7 +43,7 @@ async def main(room_url: str, token: str):
         api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py

@@ -141,6 +141,7 @@ async def dial(request: RoomRequest, raw_request: Request):
             "display_name": request.From,
             "sip_mode": "dial-in",
             "num_endpoints": 2 if request.call_transfer is not None else 1,
+            "codecs": {"audio": ["OPUS"]},
         }
         daily_room_properties["sip"] = sip_config
 

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js

@@ -103,6 +103,7 @@ export default async function handler(req, res) {
         display_name: From,
         sip_mode: 'dial-in',
         num_endpoints: call_transfer !== null ? 2 : 1,
+        codecs: {"audio": ["OPUS"]},
       };
       daily_room_properties.sip = sip_config;
     }
@@ -172,4 +173,4 @@ export const config = {
       sizeLimit: '1mb',
     },
   },
-};
+};

examples/deployment/pipecat-cloud-example/bot.py

@@ -61,7 +61,7 @@ async def main(room_url: str, token: str):
         api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/01-say-one-thing.py

@@ -9,10 +9,11 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.frames.frames import EndFrame, TranscriptionFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport

examples/foundational/02-llm-say-one-thing.py

@@ -38,7 +38,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/05-sync-speech-and-image.py

@@ -85,7 +85,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     # Create an HTTP session for API calls
     async with aiohttp.ClientSession() as session:
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/05a-local-sync-speech-and-image.py

@@ -93,7 +93,7 @@ async def main():
                         self.frame = frame
                     await self.push_frame(frame, direction)
 
-            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
             tts = CartesiaHttpTTSService(
                 api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/06-listen-and-respond.py

@@ -10,7 +10,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, MetricsFrame
+from pipecat.frames.frames import Frame, MetricsFrame, TranscriptionFrame, TTSSpeakFrame
 from pipecat.metrics.metrics import (
     LLMUsageMetricsData,
     ProcessingMetricsData,
@@ -32,7 +32,30 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
+# Custom processor that prints a message if it receives a TranscriptionFrame that says "banana"
+class BananaProcessor(FrameProcessor):
+    """A custom processor that listens for transcription frames containing the word 'banana'."""
+
+    def __init__(self):
+        super().__init__()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        # Ensure the super method is called first
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            logger.debug(f"Received transcription frame: {frame.text}")
+            if "banana" in frame.text.lower():
+                logger.info("---- Received 'banana' in transcription frame")
+
+        # Push the frame after processing
+        await self.push_frame(frame)
+
+
 class MetricsLogger(FrameProcessor):
+    def __init__(self):
+        super().__init__()
+
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -73,7 +96,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     ml = MetricsLogger()
 
@@ -87,10 +110,13 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     context = OpenAILLMContext(messages)
     context_aggregator = llm.create_context_aggregator(context)
 
+    banana = BananaProcessor()
+
     pipeline = Pipeline(
         [
             transport.input(),
             stt,
+            banana,
             context_aggregator.user(),
             llm,
             tts,

examples/foundational/06a-image-sync.py

@@ -91,7 +91,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07-interruptible.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07a-interruptible-vad.py

@@ -44,7 +44,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07b-interruptible-langchain.py

@@ -74,7 +74,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             ("human", "{input}"),
         ]
     )
-    chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
+    chain = prompt | ChatOpenAI(model="gpt-4.1", temperature=0.7)
     history_chain = RunnableWithMessageHistory(
         chain,
         get_session_history,

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

@@ -48,7 +48,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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

@@ -42,7 +42,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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

@@ -49,7 +49,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             aiohttp_session=session,
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

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

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07e-interruptible-playht-http.py

@@ -46,7 +46,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07e-interruptible-playht.py

@@ -48,7 +48,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         params=PlayHTTTSService.InputParams(language=Language.EN),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07g-interruptible-openai.py

@@ -46,7 +46,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07h-interruptible-openpipe.py

@@ -50,7 +50,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     llm = OpenPipeLLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
         openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
-        model="gpt-4o",
         tags={"conversation_id": f"pipecat-{timestamp}"},
     )
 

examples/foundational/07i-interruptible-xtts.py

@@ -49,7 +49,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             base_url="http://localhost:8000",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

examples/foundational/07j-interruptible-gladia.py

@@ -54,7 +54,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
 
     messages = [
         {

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

@@ -42,7 +42,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07m-interruptible-polly.py

@@ -48,7 +48,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07o-interruptible-assemblyai.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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

@@ -44,7 +44,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07q-interruptible-rime-http.py

@@ -49,7 +49,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             aiohttp_session=session,
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

examples/foundational/07q-interruptible-rime.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="rex",
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07t-interruptible-fish.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07v-interruptible-neuphonic-http.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07v-interruptible-neuphonic.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07w-interruptible-fal.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/07x-interruptible-local.py

@@ -45,7 +45,7 @@ async def main():
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/10-wake-phrase.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/11-sound-effects.py

@@ -93,7 +93,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/12b-describe-video-gpt-4o.py

@@ -74,7 +74,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     # OpenAI GPT-4o for vision analysis
-    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/14-function-calling.py

@@ -53,7 +53,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/14d-function-calling-video.py

@@ -82,7 +82,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     llm.register_function("get_weather", get_weather)
     llm.register_function("get_image", get_image)
 

examples/foundational/15-switch-voices.py

@@ -83,7 +83,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     llm.register_function("switch_voice", switch_voice)
 
     tools = [

examples/foundational/15a-switch-languages.py

@@ -73,7 +73,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9",  # Spanish-speaking Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     llm.register_function("switch_language", switch_language)
 
     tools = [

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

@@ -6,7 +6,6 @@
 
 import os
 
-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -40,105 +39,101 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         ),
     )
 
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-        tts = DeepgramTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-asteria-en",
-            base_url="http://0.0.0.0:8080/v1/speak",
-        )
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        voice="aura-asteria-en",
+        base_url="http://0.0.0.0:8080",
+    )
 
-        llm = OpenAILLMService(
-            # To use OpenAI
-            # api_key=os.getenv("OPENAI_API_KEY"),
-            # model="gpt-4o"
-            # Or, to use a local vLLM (or similar) api server
-            model="meta-llama/Meta-Llama-3-8B-Instruct",
-            base_url="http://0.0.0.0:8000/v1",
-        )
+    llm = OpenAILLMService(
+        # To use OpenAI
+        # api_key=os.getenv("OPENAI_API_KEY"),
+        # Or, to use a local vLLM (or similar) api server
+        model="meta-llama/Meta-Llama-3-8B-Instruct",
+        base_url="http://0.0.0.0:8000/v1",
+    )
 
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),
         ]
+    )
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+        ),
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),
-            ]
-        )
+    # When the first participant joins, the bot should introduce itself.
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
-
-        # When the first participant joins, the bot should introduce itself.
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        # Handle "latency-ping" messages. The client will send app messages that look like
-        # this:
-        #   { "latency-ping": { ts: <client-side timestamp> }}
-        #
-        # We want to send an immediate pong back to the client from this handler function.
-        # Also, we will push a frame into the top of the pipeline and send it after the
-        #
-        @transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            try:
-                if "latency-ping" in message:
-                    logger.debug(f"Received latency ping app message: {message}")
-                    ts = message["latency-ping"]["ts"]
-                    # Send immediately
-                    transport.output().send_message(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
-                        )
+    # Handle "latency-ping" messages. The client will send app messages that look like
+    # this:
+    #   { "latency-ping": { ts: <client-side timestamp> }}
+    #
+    # We want to send an immediate pong back to the client from this handler function.
+    # Also, we will push a frame into the top of the pipeline and send it after the
+    #
+    @transport.event_handler("on_app_message")
+    async def on_app_message(transport, message, sender):
+        try:
+            if "latency-ping" in message:
+                logger.debug(f"Received latency ping app message: {message}")
+                ts = message["latency-ping"]["ts"]
+                # Send immediately
+                transport.output().send_message(
+                    DailyTransportMessageFrame(
+                        message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
                     )
-                    # And push to the pipeline for the Daily transport.output to send
-                    await task.queue_frame(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                            participant_id=sender,
-                        )
+                )
+                # And push to the pipeline for the Daily transport.output to send
+                await task.queue_frame(
+                    DailyTransportMessageFrame(
+                        message={"latency-pong-pipeline-delivery": {"ts": ts}},
+                        participant_id=sender,
                     )
-            except Exception as e:
-                logger.debug(f"message handling error: {e} - {message}")
+                )
+        except Exception as e:
+            logger.debug(f"message handling error: {e} - {message}")
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
 
-        @transport.event_handler("on_client_closed")
-        async def on_client_closed(transport, client):
-            logger.info(f"Client closed connection")
-            await task.cancel()
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=False)
+    runner = PipelineRunner(handle_sigint=False)
 
-        await runner.run(task)
+    await runner.run(task)
 
 
 if __name__ == "__main__":

examples/foundational/17-detect-user-idle.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/20a-persistent-context-openai.py

@@ -185,7 +185,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # you can either register a single function for all function calls, or specific functions
     # llm.register_function(None, fetch_weather_from_api)

examples/foundational/22-natural-conversation.py

@@ -56,7 +56,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     # statement. This doesn't really need to be an LLM, we could use NLP
     # libraries for that, but it was easier as an example because we
     # leverage the context aggregators.
-    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     statement_messages = [
         {
@@ -69,7 +69,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
 
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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

@@ -224,10 +224,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     # This is the LLM that will be used to detect if the user has finished a
     # statement. This doesn't really need to be an LLM, we could use NLP
     # libraries for that, but we have the machinery to use an LLM, so we might as well!
-    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
     llm.register_function("get_current_weather", fetch_weather_from_api)

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

@@ -428,16 +428,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     # This is the LLM that will be used to detect if the user has finished a
     # statement. This doesn't really need to be an LLM, we could use NLP
     # libraries for that, but we have the machinery to use an LLM, so we might as well!
-    statement_llm = AnthropicLLMService(
-        api_key=os.getenv("ANTHROPIC_API_KEY"),
-        model="claude-3-5-sonnet-20241022",
-    )
+    statement_llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
 
     # This is the regular LLM.
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o",
-    )
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # Register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
     llm.register_function("get_current_weather", fetch_weather_from_api)

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

@@ -33,7 +33,10 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMAssistantResponseAggregator,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -478,7 +481,7 @@ class LLMAggregatorBuffer(LLMAssistantResponseAggregator):
     """Buffers the output of the transcription LLM. Used by the bot output gate."""
 
     def __init__(self, **kwargs):
-        super().__init__(expect_stripped_words=False)
+        super().__init__(params=LLMAssistantAggregatorParams(expect_stripped_words=False))
         self._transcription = ""
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):

examples/foundational/23-bot-background-sound-daily.py

@@ -62,7 +62,7 @@ async def main():
             voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

examples/foundational/23-bot-background-sound-p2p.py

@@ -4,15 +4,13 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""
-Usage
+"""Usage
 -----
 Set the path to your background audio file using the `INPUT_AUDIO_PATH` environment variable, then run the bot using:
 
     INPUT_AUDIO_PATH=path/to/your_audio.mp3 python 23-bot-background-sound.py
 
 Example:
-
     INPUT_AUDIO_PATH=my_audio.mp3 python 23-bot-background-sound.py
 """
 
@@ -71,7 +69,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/24-stt-mute-filter.py

@@ -64,7 +64,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     llm.register_function("get_current_weather", fetch_weather_from_api)
 
     weather_function = FunctionSchema(

examples/foundational/28-transcription-processor.py

@@ -109,10 +109,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o",
-    )
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/29-livekit-audio-chat.py

@@ -127,7 +127,7 @@ async def main():
             ),
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/30-observer.py

@@ -88,7 +88,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/foundational/35-pattern-pair-voice-switching.py

@@ -120,7 +120,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     )
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # System prompt for storytelling with voice switching
     system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.

examples/foundational/36-user-email-gathering.py

@@ -63,7 +63,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     #     aiohttp_session=session,
     # )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # You can aslo register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
     llm.register_function("store_user_emails", store_user_emails)

examples/foundational/37-mem0.py

@@ -210,10 +210,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     @rtvi.event_handler("on_client_ready")
     async def on_client_ready(rtvi):
         await rtvi.set_bot_ready()
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
         # Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
         greeting = await get_initial_greeting(
             memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
@@ -225,6 +221,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         # Queue the context frame to start the conversation
         await task.queue_frames([context_aggregator.user().get_context_frame()])
 
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")

examples/foundational/38-smart-turn.py

@@ -0,0 +1,111 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn import SmartTurnAnalyzer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+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.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection):
+    logger.info(f"Starting bot")
+
+    remote_smart_turn_url = os.getenv("REMOTE_SMART_TURN_URL")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+            vad_audio_passthrough=True,
+            turn_analyzer=SmartTurnAnalyzer(url=remote_smart_turn_url),
+        ),
+    )
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

examples/foundational/38a-local-smart-turn.py

@@ -0,0 +1,129 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.local_smart_turn import LocalCoreMLSmartTurnAnalyzer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+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.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection):
+    logger.info(f"Starting bot")
+
+    # To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
+    # to the path where the smart-turn repo is cloned.
+    #
+    # Example setup:
+    #
+    #   # Git LFS (Large File Storage)
+    #   brew install git-lfs
+    #   # Hugging Face uses LFS to store large model files, including .mlpackage
+    #   git lfs install
+    #   # Clone the repo with the smart_turn_classifier.mlpackage
+    #   git clone https://huggingface.co/pipecat-ai/smart-turn
+    #
+    # Then set the env variable:
+    #   export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
+    # or add it to your .env file
+    smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+            vad_audio_passthrough=True,
+            turn_analyzer=LocalCoreMLSmartTurnAnalyzer(
+                smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
+            ),
+        ),
+    )
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

examples/instant-voice/server/src/single_bot.py

@@ -98,14 +98,16 @@ async def main():
     @rtvi.event_handler("on_client_ready")
     async def on_client_ready(rtvi):
         await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @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()])
+        logger.debug("First participant joined: {}", participant["id"])
 
     @daily_transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):
-        print(f"Participant left: {participant}")
+        logger.debug(f"Participant left: {participant}")
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=False)

examples/moondream-chatbot/bot.py

@@ -156,7 +156,7 @@ async def main():
             voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         ta = TalkingAnimation()
 

examples/news-chatbot/server/news_bot.py

@@ -148,10 +148,13 @@ async def main():
         @rtvi.event_handler("on_client_ready")
         async def on_client_ready(rtvi):
             await rtvi.set_bot_ready()
+            # Kick off the conversation
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @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()])
+            logger.debug("First participant joined: {}", participant["id"])
+            await transport.capture_participant_transcription(participant["id"])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/p2p-webrtc/daily-interop-bridge/README.md

@@ -0,0 +1,61 @@
+# SmallWebRTC and Daily
+
+A Pipecat example demonstrating how to interoperate audio and video between `SmallWebRTCTransport` and `DailyTransport`.
+
+## 🚀 Quick Start
+
+### 1️⃣ Start the Bot Server
+
+#### 🔧 Set Up the Environment
+1. Create and activate a virtual environment:
+   ```bash
+   python3 -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Configure environment variables:
+   - Copy `env.example` to `.env`
+   ```bash
+   cp env.example .env
+   ```
+   - Add your API keys
+
+#### ▶️ Run the Server
+```bash
+python server.py
+```
+
+###  1️⃣ Connect the first client using Daily Prebuilt
+
+- Open your browser and navigate to the same URL that you configured inside your `.env` file:
+  - `DAILY_SAMPLE_ROOM_URL`
+
+### 2️⃣ Connect the second client using SmallWebRTC Prebuilt UI
+
+- Open your browser and navigate to:
+👉 http://localhost:7860
+  - (Or use your custom port, if configured)
+
+## ⚠️ Important Note
+Ensure the bot server is running before using any client implementations.
+
+## 📌 Requirements
+
+- Python **3.10+**
+- Node.js **16+** (for JavaScript components)
+- Google API Key
+- Modern web browser with WebRTC support
+
+---
+
+### 💡 Notes
+- Ensure all dependencies are installed before running the server.
+- Check the `.env` file for missing configurations.
+- WebRTC requires a secure environment (HTTPS) for full functionality in production.
+
+Happy coding! 🎉

examples/p2p-webrtc/daily-interop-bridge/bot.py

@@ -0,0 +1,128 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.frames.frames import (
+    InputAudioRawFrame,
+    InputImageRawFrame,
+    OutputAudioRawFrame,
+    OutputImageRawFrame,
+)
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+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 Frame, FrameDirection, FrameProcessor
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class MirrorProcessor(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, InputAudioRawFrame):
+            await self.push_frame(
+                OutputAudioRawFrame(
+                    audio=frame.audio,
+                    sample_rate=frame.sample_rate,
+                    num_channels=frame.num_channels,
+                )
+            )
+        elif isinstance(frame, InputImageRawFrame):
+            await self.push_frame(
+                OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+async def run_bot(webrtc_connection):
+    pipecat_transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            camera_in_enabled=True,
+            camera_out_enabled=True,
+            camera_out_is_live=True,
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_width=1280,
+            camera_out_height=720,
+            vad_enabled=False,
+        ),
+    )
+
+    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
+    daily_transport = DailyTransport(
+        room_url,
+        None,
+        "SmallWebRTC",
+        params=DailyParams(
+            camera_in_enabled=True,
+            camera_out_enabled=True,
+            camera_out_is_live=True,
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_width=1280,
+            camera_out_height=720,
+            vad_enabled=False,
+        ),
+    )
+
+    pipeline = Pipeline(
+        [
+            ParallelPipeline(
+                [
+                    daily_transport.input(),
+                    MirrorProcessor(),
+                    pipecat_transport.output(),
+                ],
+                [
+                    pipecat_transport.input(),
+                    MirrorProcessor(),
+                    daily_transport.output(),
+                ],
+            )
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=False,
+        ),
+    )
+
+    @daily_transport.event_handler("on_participant_joined")
+    async def on_participant_joined(transport, participant):
+        await transport.capture_participant_video(participant["id"])
+
+    @pipecat_transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Pipecat Client connected")
+
+    @pipecat_transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Pipecat Client disconnected")
+
+    @pipecat_transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info("Pipecat Client closed")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)

examples/p2p-webrtc/daily-interop-bridge/env.example

@@ -0,0 +1,2 @@
+DAILY_API_KEY=
+DAILY_SAMPLE_ROOM_URL=

examples/p2p-webrtc/daily-interop-bridge/requirements.txt

@@ -0,0 +1,5 @@
+python-dotenv
+fastapi[all]
+uvicorn
+aiortc
+pipecat-ai[silero, webrtc, daily]

examples/p2p-webrtc/daily-interop-bridge/server.py

@@ -0,0 +1,89 @@
+import argparse
+import asyncio
+import logging
+from contextlib import asynccontextmanager
+from typing import Dict
+
+import uvicorn
+from bot import run_bot
+from dotenv import load_dotenv
+from fastapi import BackgroundTasks, FastAPI
+from fastapi.responses import RedirectResponse
+from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
+
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+# Load environment variables
+load_dotenv(override=True)
+
+logger = logging.getLogger("pc")
+
+app = FastAPI()
+
+# Store connections by pc_id
+pcs_map: Dict[str, SmallWebRTCConnection] = {}
+
+ice_servers = ["stun:stun.l.google.com:19302"]
+
+# Mount the frontend at /
+app.mount("/prebuilt", SmallWebRTCPrebuiltUI)
+
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+    return RedirectResponse(url="/prebuilt/")
+
+
+@app.post("/api/offer")
+async def offer(request: dict, background_tasks: BackgroundTasks):
+    pc_id = request.get("pc_id")
+
+    if pc_id and pc_id in pcs_map:
+        pipecat_connection = pcs_map[pc_id]
+        logger.info(f"Reusing existing connection for pc_id: {pc_id}")
+        await pipecat_connection.renegotiate(
+            sdp=request["sdp"], type=request["type"], restart_pc=request.get("restart_pc", False)
+        )
+    else:
+        pipecat_connection = SmallWebRTCConnection(ice_servers)
+        await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
+
+        @pipecat_connection.event_handler("closed")
+        async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
+            logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
+            pcs_map.pop(webrtc_connection.pc_id, None)
+
+        background_tasks.add_task(run_bot, pipecat_connection)
+
+    answer = pipecat_connection.get_answer()
+    # Updating the peer connection inside the map
+    pcs_map[answer["pc_id"]] = pipecat_connection
+
+    return answer
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    yield  # Run app
+    coros = [pc.close() for pc in pcs_map.values()]
+    await asyncio.gather(*coros)
+    pcs_map.clear()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="WebRTC demo")
+    parser.add_argument(
+        "--host", default="localhost", help="Host for HTTP server (default: localhost)"
+    )
+    parser.add_argument(
+        "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
+    )
+    parser.add_argument("--verbose", "-v", action="count")
+    args = parser.parse_args()
+
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+    else:
+        logging.basicConfig(level=logging.INFO)
+
+    uvicorn.run(app, host=args.host, port=args.port)

examples/p2p-webrtc/video-transform/server/bot.py

@@ -135,12 +135,12 @@ async def run_bot(webrtc_connection):
     async def on_client_ready(rtvi):
         logger.info("Pipecat client ready.")
         await rtvi.set_bot_ready()
+        # Kick off the conversation.
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @pipecat_transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info("Pipecat Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @pipecat_transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/p2p-webrtc/voice-agent/index.html

@@ -40,7 +40,9 @@
         const createSmallWebRTCConnection = async (audioTrack) => {
             const pc = new RTCPeerConnection()
             pc.ontrack = e => audioEl.srcObject = e.streams[0]
+            // SmallWebRTCTransport expects to receive both transceivers
             pc.addTransceiver(audioTrack, { direction: 'sendrecv' })
+            pc.addTransceiver('video', { direction: 'sendrecv' })
             await pc.setLocalDescription(await pc.createOffer())
             //await waitForIceGatheringComplete(pc)
             const offer = pc.localDescription

examples/patient-intake/bot.py

@@ -324,7 +324,7 @@ async def main():
         #     voice_id="846d6cb0-2301-48b6-9683-48f5618ea2f6",  # Spanish-speaking Lady
         # )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = []
         context = OpenAILLMContext(messages=messages)

examples/phone-chatbot/bot_twilio.py

@@ -60,7 +60,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

examples/phone-chatbot/call_transfer.py

@@ -305,7 +305,7 @@ async def main(
     tools = ToolsSchema(standard_tools=[terminate_call_function, dial_operator_function])
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # Register functions with the LLM
     llm.register_function(

examples/phone-chatbot/simple_dialin.py

@@ -129,7 +129,7 @@ async def main(
     system_instruction = """You are 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, but keep your responses brief. Start by introducing yourself. If the user ends the conversation, **IMMEDIATELY** call the `terminate_call` function. """
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # Register functions with the LLM
     llm.register_function("terminate_call", terminate_call)

examples/phone-chatbot/simple_dialout.py

@@ -101,7 +101,7 @@ async def main(
     system_instruction = """You are 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, but keep your responses brief. Start by introducing yourself. If the user ends the conversation, **IMMEDIATELY** call the `terminate_call` function. """
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # Register functions with the LLM
     llm.register_function("terminate_call", terminate_call)

examples/sentry-metrics/bot.py

@@ -63,7 +63,6 @@ async def main():
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o",
             metrics=SentryMetrics(),
         )
 

examples/simple-chatbot/client/react/src/components/DebugDisplay.tsx

@@ -76,7 +76,7 @@ export function DebugDisplay() {
   );
 
   useRTVIClientEvent(
-    RTVIEvent.TrackedStopped,
+    RTVIEvent.TrackStopped,
     useCallback(
       (track: MediaStreamTrack, participant?: Participant) => {
         log(

examples/simple-chatbot/server/README.md

@@ -70,3 +70,17 @@ Run the server:
 ```bash
 python server.py
 ```
+
+## Troubleshooting
+
+If you encounred this error:
+
+```bash
+aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host api.daily.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1000)')]
+```
+
+It's because Python cannot verify the SSL certificate from https://api.daily.co when making a POST request to create a room or token.
+
+This is a common issue when the system doesn't have the proper CA certificates.
+
+Install SSL Certificates (macOS): `/Applications/Python\ 3.12/Install\ Certificates.command`

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

@@ -183,11 +183,12 @@ async def main():
         @rtvi.event_handler("on_client_ready")
         async def on_client_ready(rtvi):
             await rtvi.set_bot_ready()
+            # Kick off the conversation
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

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

@@ -155,7 +155,7 @@ async def main():
         )
 
         # Initialize LLM service
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {
@@ -210,11 +210,12 @@ async def main():
         @rtvi.event_handler("on_client_ready")
         async def on_client_ready(rtvi):
             await rtvi.set_bot_ready()
+            # Kick off the conversation
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/telnyx-chatbot/bot.py

@@ -48,7 +48,7 @@ async def run_bot(
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 

examples/translation-chatbot/bot.py

@@ -150,7 +150,7 @@ async def main():
         in_language = "English"
         out_language = "Spanish"
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
         context = OpenAILLMContext()
         context_aggregator = llm.create_context_aggregator(context)
 

examples/twilio-chatbot/bot.py

@@ -68,7 +68,7 @@ async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool):
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
 

examples/twilio-chatbot/client.py

@@ -98,7 +98,7 @@ async def run_client(client_name: str, server_url: str, duration_secs: int):
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # We let the audio passthrough so we can record the conversation.
     stt = DeepgramSTTService(

examples/websocket-server/bot.py

@@ -91,7 +91,7 @@ async def main():
         )
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 

pyproject.toml

@@ -54,7 +54,7 @@ fal = [ "fal-client~=0.5.9" ]
 fireworks = []
 fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
 gladia = [ "websockets~=13.1" ]
-google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4" ]
+google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4", "websockets~=13.1" ]
 grok = []
 groq = [ "groq~=0.20.0" ]
 gstreamer = [ "pygobject~=3.50.0" ]
@@ -79,6 +79,8 @@ qwen = []
 rime = [ "websockets~=13.1" ]
 riva = [ "nvidia-riva-client~=2.19.0" ]
 sentry = [ "sentry-sdk~=2.23.1" ]
+local-smart-turn = [ "coremltools>=8.0", "transformers", "torch==2.5.0", "torchaudio==2.5.0" ]
+remote-smart-turn = []
 silero = [ "onnxruntime~=1.20.1" ]
 simli = [ "simli-ai~=0.1.10"]
 soundfile = [ "soundfile~=0.13.0" ]

src/pipecat/audio/mixers/soundfile_mixer.py

@@ -38,9 +38,11 @@ class SoundfileMixer(BaseAudioMixer):
 
     def __init__(
         self,
+        *,
         sound_files: Mapping[str, str],
         default_sound: str,
         volume: float = 0.4,
+        mixing: bool = True,
         loop: bool = True,
         **kwargs,
     ):
@@ -52,7 +54,7 @@ class SoundfileMixer(BaseAudioMixer):
         self._sound_pos = 0
         self._sounds: Dict[str, Any] = {}
         self._current_sound = default_sound
-        self._mixing = True
+        self._mixing = mixing
         self._loop = loop
 
     async def start(self, sample_rate: int):

src/pipecat/audio/turn/base_smart_turn.py

@@ -0,0 +1,168 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import time
+from abc import abstractmethod
+from typing import Dict, Optional
+
+import numpy as np
+from loguru import logger
+from pydantic import BaseModel
+
+from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer, EndOfTurnState
+
+# Default timing parameters
+STOP_SECS = 3
+PRE_SPEECH_MS = 0
+MAX_DURATION_SECONDS = 8  # Max allowed segment duration
+USE_ONLY_LAST_VAD_SEGMENT = True
+
+
+class SmartTurnParams(BaseModel):
+    stop_secs: float = STOP_SECS
+    pre_speech_ms: float = PRE_SPEECH_MS
+    max_duration_secs: float = MAX_DURATION_SECONDS
+    # not exposing this for now yet until the model can handle it.
+    # use_only_last_vad_segment: bool = USE_ONLY_LAST_VAD_SEGMENT
+
+
+class BaseSmartTurn(BaseTurnAnalyzer):
+    def __init__(
+        self, *, sample_rate: Optional[int] = None, params: SmartTurnParams = SmartTurnParams()
+    ):
+        super().__init__(sample_rate=sample_rate)
+        self._params = params
+        # Configuration
+        self._stop_ms = self._params.stop_secs * 1000  # silence threshold in ms
+        # Inference state
+        self._audio_buffer = []
+        self._speech_triggered = False
+        self._silence_ms = 0
+        self._speech_start_time = None
+
+    @property
+    def speech_triggered(self) -> bool:
+        return self._speech_triggered
+
+    def append_audio(self, buffer: bytes, is_speech: bool) -> EndOfTurnState:
+        # Convert raw audio to float32 format and append to the buffer
+        audio_int16 = np.frombuffer(buffer, dtype=np.int16)
+        audio_float32 = np.frombuffer(audio_int16, dtype=np.int16).astype(np.float32) / 32768.0
+        self._audio_buffer.append((time.time(), audio_float32))
+
+        state = EndOfTurnState.INCOMPLETE
+
+        if is_speech:
+            # Reset silence tracking on speech
+            self._silence_ms = 0
+            self._speech_triggered = True
+            if self._speech_start_time is None:
+                self._speech_start_time = time.time()
+                logger.debug(f"Speech started at {self._speech_start_time}")
+        else:
+            if self._speech_triggered:
+                chunk_duration_ms = len(audio_int16) / (self._sample_rate / 1000)
+                self._silence_ms += chunk_duration_ms
+                # If silence exceeds threshold, mark end of turn
+                if self._silence_ms >= self._stop_ms:
+                    logger.debug(
+                        f"End of Turn complete due to stop_secs. Silence in ms: {self._silence_ms}"
+                    )
+                    state = EndOfTurnState.COMPLETE
+                    self._clear(state)
+            else:
+                # Trim buffer to prevent unbounded growth before speech
+                max_buffer_time = (
+                    (self._params.pre_speech_ms / 1000)
+                    + self._params.stop_secs
+                    + self._params.max_duration_secs
+                )
+                while (
+                    self._audio_buffer and self._audio_buffer[0][0] < time.time() - max_buffer_time
+                ):
+                    self._audio_buffer.pop(0)
+
+        return state
+
+    def analyze_end_of_turn(self) -> EndOfTurnState:
+        logger.debug("Analyzing End of Turn...")
+        state = self._process_speech_segment(self._audio_buffer)
+        if state == EndOfTurnState.COMPLETE or USE_ONLY_LAST_VAD_SEGMENT:
+            self._clear(state)
+        logger.debug(f"End of Turn result: {state}")
+        return state
+
+    def _clear(self, turn_state: EndOfTurnState):
+        # Reset internal state for next turn
+        logger.debug("Clearing audio buffer...")
+        # If the state is still incomplete, keep the _speech_triggered as True
+        self._speech_triggered = turn_state == EndOfTurnState.INCOMPLETE
+        self._audio_buffer = []
+        self._speech_start_time = None
+        self._silence_ms = 0
+
+    def _process_speech_segment(self, audio_buffer) -> EndOfTurnState:
+        state = EndOfTurnState.INCOMPLETE
+
+        if not audio_buffer:
+            return state
+
+        # Extract recent audio segment for prediction
+        start_time = self._speech_start_time - (self._params.pre_speech_ms / 1000)
+        start_index = 0
+        for i, (t, _) in enumerate(audio_buffer):
+            if t >= start_time:
+                start_index = i
+                break
+
+        end_index = len(audio_buffer) - 1
+
+        # Extract the audio segment
+        segment_audio_chunks = [chunk for _, chunk in audio_buffer[start_index : end_index + 1]]
+        segment_audio = np.concatenate(segment_audio_chunks)
+
+        logger.debug(f"Segment audio chunks after start index: {len(segment_audio)}")
+
+        # Limit maximum duration
+        max_samples = int(self._params.max_duration_secs * self.sample_rate)
+        if len(segment_audio) > max_samples:
+            # slices the array to keep the last max_samples samples, discarding the earlier part.
+            segment_audio = segment_audio[-max_samples:]
+
+        logger.debug(f"Segment audio chunks after limiting duration: {len(segment_audio)}")
+
+        if len(segment_audio) > 0:
+            start_time = time.perf_counter()
+            result = self._predict_endpoint(segment_audio)
+            state = (
+                EndOfTurnState.COMPLETE if result["prediction"] == 1 else EndOfTurnState.INCOMPLETE
+            )
+            end_time = time.perf_counter()
+
+            logger.debug("--------")
+            logger.debug(f"Prediction: {'Complete' if result['prediction'] == 1 else 'Incomplete'}")
+            logger.debug(f"Probability of complete: {result['probability']:.4f}")
+            logger.debug(f"Prediction took {(end_time - start_time) * 1000:.2f}ms seconds")
+        else:
+            logger.debug(f"params: {self._params}, stop_ms: {self._stop_ms}")
+            logger.debug("Captured empty audio segment, skipping prediction.")
+
+        return state
+
+    @abstractmethod
+    def _predict_endpoint(self, buffer: np.ndarray) -> Dict[str, any]:
+        """
+        Abstract method to predict if a turn has ended based on audio.
+
+        Args:
+            buffer: Float32 numpy array of audio samples at 16kHz.
+
+        Returns:
+            Dictionary with:
+              - prediction: 1 if turn is complete, else 0
+              - probability: Confidence of the prediction
+        """
+        pass

src/pipecat/audio/turn/base_turn_analyzer.py

@@ -0,0 +1,81 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Optional
+
+
+class EndOfTurnState(Enum):
+    COMPLETE = 1
+    INCOMPLETE = 2
+
+
+class BaseTurnAnalyzer(ABC):
+    """
+    Abstract base class for analyzing user end of turn.
+    """
+
+    def __init__(self, *, sample_rate: Optional[int] = None):
+        self._init_sample_rate = sample_rate
+        self._sample_rate = 0
+
+    @property
+    def sample_rate(self) -> int:
+        """
+        Returns the current sample rate.
+
+        Returns:
+            int: The effective sample rate for audio processing.
+        """
+        return self._sample_rate
+
+    def set_sample_rate(self, sample_rate: int):
+        """
+        Sets the sample rate for audio processing.
+
+        If the initial sample rate was provided, it will use that; otherwise, it sets to
+        the provided sample rate.
+
+        Args:
+            sample_rate (int): The sample rate to set.
+        """
+        self._sample_rate = self._init_sample_rate or sample_rate
+
+    @property
+    @abstractmethod
+    def speech_triggered(self) -> bool:
+        """
+        Determines if speech has been detected.
+
+        Returns:
+            bool: True if speech is triggered, otherwise False.
+        """
+        pass
+
+    @abstractmethod
+    def append_audio(self, buffer: bytes, is_speech: bool) -> EndOfTurnState:
+        """
+        Appends audio data for analysis.
+
+        Args:
+            buffer (bytes): The audio data to append.
+            is_speech (bool): Indicates whether the appended audio is speech or not.
+
+        Returns:
+            EndOfTurnState: The resulting state after appending the audio.
+        """
+        pass
+
+    @abstractmethod
+    def analyze_end_of_turn(self) -> EndOfTurnState:
+        """
+        Analyzes if an end of turn has occurred based on the audio input.
+
+        Returns:
+            EndOfTurnState: The result of the end of turn analysis.
+        """
+        pass

src/pipecat/audio/turn/local_smart_turn.py

@@ -0,0 +1,65 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+from typing import Dict
+
+import numpy as np
+import torch
+from loguru import logger
+
+from pipecat.audio.turn.base_smart_turn import BaseSmartTurn
+
+try:
+    import coremltools as ct
+    from transformers import AutoFeatureExtractor
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error(
+        "In order to use the LocalSmartTurnAnalyzer, you need to `pip install pipecat-ai[local-smart-turn]`."
+    )
+    raise Exception(f"Missing module: {e}")
+
+
+class LocalCoreMLSmartTurnAnalyzer(BaseSmartTurn):
+    def __init__(self, smart_turn_model_path: str, **kwargs):
+        super().__init__(**kwargs)
+
+        if not smart_turn_model_path:
+            logger.error("smart_turn_model_path is not set.")
+            raise Exception("smart_turn_model_path must be provided.")
+
+        core_ml_model_path = f"{smart_turn_model_path}/coreml/smart_turn_classifier.mlpackage"
+
+        logger.debug("Loading Local Smart Turn model...")
+        # Only load the processor, not the torch model
+        self._turn_processor = AutoFeatureExtractor.from_pretrained(smart_turn_model_path)
+        self._turn_model = ct.models.MLModel(core_ml_model_path)
+        logger.debug("Loaded Local Smart Turn")
+
+    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
+        inputs = self._turn_processor(
+            audio_array,
+            sampling_rate=16000,
+            padding="max_length",
+            truncation=True,
+            max_length=800,  # Maximum length as specified in training
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+
+        output = self._turn_model.predict(dict(inputs))
+        logits = output["logits"]  # Core ML returns numpy array
+        logits_tensor = torch.tensor(logits)
+        probabilities = torch.nn.functional.softmax(logits_tensor, dim=1)
+        completion_prob = probabilities[0, 1].item()  # Probability of class 1 (Complete)
+        prediction = 1 if completion_prob > 0.5 else 0
+
+        return {
+            "prediction": prediction,
+            "probability": completion_prob,
+        }

src/pipecat/audio/turn/smart_turn.py

@@ -0,0 +1,75 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import io
+import os
+from typing import Dict
+
+import numpy as np
+import requests
+from loguru import logger
+
+from pipecat.audio.turn.base_smart_turn import BaseSmartTurn
+
+
+class SmartTurnAnalyzer(BaseSmartTurn):
+    def __init__(self, url: str, **kwargs):
+        super().__init__(**kwargs)
+        self.remote_smart_turn_url = url
+
+        if not self.remote_smart_turn_url:
+            logger.error("remote_smart_turn_url is not set.")
+            raise Exception("remote_smart_turn_url must be provided.")
+
+        # Use a session to reuse connections (keep-alive)
+        self.session = requests.Session()
+        self.session.headers.update({"Connection": "keep-alive"})
+
+    def _serialize_array(self, audio_array: np.ndarray) -> bytes:
+        logger.trace("Serializing NumPy array to bytes...")
+        buffer = io.BytesIO()
+        np.save(buffer, audio_array)
+        serialized_bytes = buffer.getvalue()
+        logger.trace(f"Serialized size: {len(serialized_bytes)} bytes")
+        return serialized_bytes
+
+    def _send_raw_request(self, data_bytes: bytes):
+        headers = {"Content-Type": "application/octet-stream"}
+        logger.trace(
+            f"Sending {len(data_bytes)} bytes as raw body to {self.remote_smart_turn_url}..."
+        )
+        try:
+            response = self.session.post(
+                self.remote_smart_turn_url,
+                data=data_bytes,
+                headers=headers,
+                timeout=60,
+            )
+
+            logger.trace("\n--- Response ---")
+            logger.trace(f"Status Code: {response.status_code}")
+
+            if response.ok:
+                try:
+                    logger.trace("Response JSON:")
+                    logger.trace(response.json())
+                    return response.json()
+                except requests.exceptions.JSONDecodeError:
+                    logger.trace("Response Content (non-JSON):")
+                    logger.trace(response.text)
+            else:
+                logger.trace("Response Content (Error):")
+                logger.trace(response.text)
+                response.raise_for_status()
+
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Failed to send raw request to Daily Smart Turn: {e}")
+            raise Exception("Failed to send raw request to Daily Smart Turn.")
+
+    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
+        serialized_array = self._serialize_array(audio_array)
+        return self._send_raw_request(serialized_array)

src/pipecat/processors/aggregators/llm_response.py

@@ -6,6 +6,7 @@
 
 import asyncio
 from abc import abstractmethod
+from dataclasses import dataclass
 from typing import Dict, List, Literal, Set
 
 from loguru import logger
@@ -46,6 +47,16 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.time import time_now_iso8601
 
 
+@dataclass
+class LLMUserAggregatorParams:
+    aggregation_timeout: float = 1.0
+
+
+@dataclass
+class LLMAssistantAggregatorParams:
+    expect_stripped_words: bool = True
+
+
 class LLMFullResponseAggregator(FrameProcessor):
     """This is an LLM aggregator that aggregates a full LLM completion. It
     aggregates LLM text frames (tokens) received between
@@ -230,11 +241,23 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
     def __init__(
         self,
         context: OpenAILLMContext,
-        aggregation_timeout: float = 1.0,
+        *,
+        params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
         **kwargs,
     ):
         super().__init__(context=context, role="user", **kwargs)
-        self._aggregation_timeout = aggregation_timeout
+        self._params = params
+        if "aggregation_timeout" in kwargs:
+            import warnings
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("always")
+                warnings.warn(
+                    "Parameter 'aggregation_timeout' is deprecated, use 'params' instead.",
+                    DeprecationWarning,
+                )
+
+            self._params.aggregation_timeout = kwargs["aggregation_timeout"]
 
         self._seen_interim_results = False
         self._user_speaking = False
@@ -357,7 +380,9 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
     async def _aggregation_task_handler(self):
         while True:
             try:
-                await asyncio.wait_for(self._aggregation_event.wait(), self._aggregation_timeout)
+                await asyncio.wait_for(
+                    self._aggregation_event.wait(), self._params.aggregation_timeout
+                )
                 await self._maybe_push_bot_interruption()
             except asyncio.TimeoutError:
                 if not self._user_speaking:
@@ -394,9 +419,27 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
 
     """
 
-    def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True, **kwargs):
+    def __init__(
+        self,
+        context: OpenAILLMContext,
+        *,
+        params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+        **kwargs,
+    ):
         super().__init__(context=context, role="assistant", **kwargs)
-        self._expect_stripped_words = expect_stripped_words
+        self._params = params
+
+        if "expect_stripped_words" in kwargs:
+            import warnings
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("always")
+                warnings.warn(
+                    "Parameter 'expect_stripped_words' is deprecated, use 'params' instead.",
+                    DeprecationWarning,
+                )
+
+            self._params.expect_stripped_words = kwargs["expect_stripped_words"]
 
         self._started = 0
         self._function_calls_in_progress: Dict[str, FunctionCallInProgressFrame] = {}
@@ -558,7 +601,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         if not self._started:
             return
 
-        if self._expect_stripped_words:
+        if self._params.expect_stripped_words:
             self._aggregation += f" {frame.text}" if self._aggregation else frame.text
         else:
             self._aggregation += frame.text
@@ -572,8 +615,14 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
 
 
 class LLMUserResponseAggregator(LLMUserContextAggregator):
-    def __init__(self, messages: List[dict] = [], **kwargs):
-        super().__init__(context=OpenAILLMContext(messages), **kwargs)
+    def __init__(
+        self,
+        messages: List[dict] = [],
+        *,
+        params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        **kwargs,
+    ):
+        super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
 
     async def push_aggregation(self):
         if len(self._aggregation) > 0:
@@ -588,8 +637,14 @@ class LLMUserResponseAggregator(LLMUserContextAggregator):
 
 
 class LLMAssistantResponseAggregator(LLMAssistantContextAggregator):
-    def __init__(self, messages: List[dict] = [], **kwargs):
-        super().__init__(context=OpenAILLMContext(messages), **kwargs)
+    def __init__(
+        self,
+        messages: List[dict] = [],
+        *,
+        params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+        **kwargs,
+    ):
+        super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
 
     async def push_aggregation(self):
         if len(self._aggregation) > 0:

src/pipecat/py.typed

@@ -0,0 +1 @@
+ 

src/pipecat/services/anthropic/llm.py

@@ -11,7 +11,7 @@ import io
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import httpx
 from loguru import logger
@@ -35,7 +35,9 @@ from pipecat.frames.frames import (
 )
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
     LLMAssistantContextAggregator,
+    LLMUserAggregatorParams,
     LLMUserContextAggregator,
 )
 from pipecat.processors.aggregators.openai_llm_context import (
@@ -49,10 +51,7 @@ try:
     from anthropic import NOT_GIVEN, AsyncAnthropic, NotGiven
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`. "
-        + "Also, set `ANTHROPIC_API_KEY` environment variable."
-    )
+    logger.error("In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`.")
     raise Exception(f"Missing module: {e}")
 
 
@@ -120,8 +119,8 @@ class AnthropicLLMService(LLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> AnthropicContextAggregatorPair:
         """Create an instance of AnthropicContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -129,12 +128,10 @@ class AnthropicLLMService(LLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator
+                parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User
+                aggregator parameters.
 
         Returns:
             AnthropicContextAggregatorPair: A pair of context aggregators, one
@@ -146,8 +143,8 @@ class AnthropicLLMService(LLMService):
 
         if isinstance(context, OpenAILLMContext):
             context = AnthropicLLMContext.from_openai_context(context)
-        user = AnthropicUserContextAggregator(context, **user_kwargs)
-        assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs)
+        user = AnthropicUserContextAggregator(context, params=user_params)
+        assistant = AnthropicAssistantContextAggregator(context, params=assistant_params)
         return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
 
     async def _process_context(self, context: OpenAILLMContext):

src/pipecat/services/aws/tts.py

@@ -231,9 +231,9 @@ class PollyTTSService(TTSService):
 
             yield TTSStartedFrame()
 
-            chunk_size = 8192
-            for i in range(0, len(audio_data), chunk_size):
-                chunk = audio_data[i : i + chunk_size]
+            CHUNK_SIZE = 1024
+            for i in range(0, len(audio_data), CHUNK_SIZE):
+                chunk = audio_data[i : i + CHUNK_SIZE]
                 if len(chunk) > 0:
                     await self.stop_ttfb_metrics()
                     frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)