example: realtime with transcripts

CHANGELOG.md

@@ -5,13 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.0.58] - 2025-02-26
+## [Unreleased]
 
 ### Added
 
-- Added track-specific audio event `on_track_audio_data` to
-  `AudioBufferProcessor` for accessing separate input and output audio tracks.
-
 - Pipecat version will now be logged on every application startup. This will
   help us identify what version we are running in case of any issues.
 
@@ -48,10 +45,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for
   the pipeline).
 
-- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input
-  in the constructor. The previous method of providing a `voice_engine` input
-  that contains the engine and protocol is deprecated by PlayHT.
-
 - The base `TTSService` class now strips leading newlines before sending text
   to the TTS provider. This change is to solve issues where some TTS providers,
   like Azure, would not output text due to newlines.
@@ -85,9 +78,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 ### Fixed
 
-- Fixed a `GoogleLLMService` that was causing an exception when sending inline
-  audio in some cases.
-
 - Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to
   disconnect from the TTS service before audio from all the contexts was
   received. This affected services like Cartesia and Rime.
@@ -134,9 +124,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 - Added Gemini support to `examples/phone-chatbot`.
 
-- Added foundational example `34-audio-recording.py` showing how to use the
-  AudioBufferProcessor callbacks to save merged and track recordings.
-
 ## [0.0.57] - 2025-02-14
 
 ### Added

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

@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.elevenlabs import ElevenLabsHttpTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = ElevenLabsHttpTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        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
-                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_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # 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_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.rime import RimeHttpTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = RimeHttpTTSService(
-            api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="rex",
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        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
-                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_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # 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_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -16,10 +16,13 @@ from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai_realtime_beta import (
     InputAudioTranscription,
     OpenAIRealtimeBetaLLMService,
@@ -140,14 +143,22 @@ Remember, your responses should be short. Just one or two sentences, usually."""
             tools,
         )
 
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        # Create transcript processor and handler
+        transcript = TranscriptProcessor()
+
         context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
+                stt,
+                transcript.user(),  # User transcripts
                 context_aggregator.user(),
                 llm,  # LLM
                 context_aggregator.assistant(),
+                transcript.assistant(),  # Assistant transcripts
                 transport.output(),  # Transport bot output
             ]
         )
@@ -162,9 +173,16 @@ Remember, your responses should be short. Just one or two sentences, usually."""
             ),
         )
 
+        # Register event handler for transcript updates
+        @transcript.event_handler("on_transcript_update")
+        async def on_transcript_update(processor, frame):
+            logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
+            for msg in frame.messages:
+                logger.debug(msg)
+
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            # await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 

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

@@ -389,7 +389,7 @@ class AudioAccumulator(FrameProcessor):
             )
             self._user_speaking = False
             context = GoogleLLMContext()
-            context.add_audio_frames_message(audio_frames=self._audio_frames)
+            context.add_audio_frames_message(text="Audio follows", audio_frames=self._audio_frames)
             await self.push_frame(OpenAILLMContextFrame(context=context))
         elif isinstance(frame, InputAudioRawFrame):
             # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest

examples/foundational/34-audio-recording.py

@@ -1,186 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Audio Recording Example with Pipecat.
-
-This example demonstrates how to record audio from a conversation between a user and an AI assistant,
-saving both merged and individual audio tracks. It showcases the AudioBufferProcessor's capabilities
-to handle both combined and separate audio streams.
-
-The example:
-    1. Sets up a basic conversation with an AI assistant
-    2. Records the entire conversation
-    3. Saves three separate WAV files:
-        - A merged recording of both participants
-        - Individual recording of user audio
-        - Individual recording of assistant audio
-
-Example usage (run from pipecat root directory):
-    $ pip install "pipecat-ai[daily,openai,cartesia,silero]"
-    $ pip install -r dev-requirements.txt
-    $ python examples/foundational/34-audio-recording.py
-
-Requirements:
-    - OpenAI API key (for GPT-4)
-    - Cartesia API key (for text-to-speech)
-    - Daily API key (for video/audio transport)
-
-    Environment variables (.env file):
-        OPENAI_API_KEY=your_openai_key
-        CARTESIA_API_KEY=your_cartesia_key
-        DAILY_API_KEY=your_daily_key
-
-The recordings will be saved in a 'recordings' directory with timestamps:
-    recordings/
-        merged_20240315_123456.wav  (Combined audio)
-        user_20240315_123456.wav    (User audio only)
-        bot_20240315_123456.wav     (Bot audio only)
-
-Note:
-    This example requires the AudioBufferProcessor with track-specific audio support,
-    which provides both 'on_audio_data' and 'on_track_audio_data' events for
-    handling merged and separate audio tracks respectively.
-"""
-
-import asyncio
-import datetime
-import io
-import os
-import sys
-import wave
-
-import aiofiles
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
-    """Save audio data to a WAV file."""
-    if len(audio) > 0:
-        with io.BytesIO() as buffer:
-            with wave.open(buffer, "wb") as wf:
-                wf.setsampwidth(2)
-                wf.setnchannels(num_channels)
-                wf.setframerate(sample_rate)
-                wf.writeframes(audio)
-            async with aiofiles.open(filename, "wb") as file:
-                await file.write(buffer.getvalue())
-        logger.info(f"Audio saved to {filename}")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Recording bot",
-            DailyParams(
-                # audio_in_enabled=True,
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,  # Enable audio passthrough for recording
-            ),
-        )
-
-        tts = CartesiaTTSService(
-            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-4")
-
-        # Create audio buffer processor
-        audiobuffer = AudioBufferProcessor()
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                audiobuffer,  # Add audio buffer to pipeline
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            await audiobuffer.start_recording()
-            messages.append(
-                {
-                    "role": "system",
-                    "content": "Greet the user and explain that this conversation will be recorded.",
-                }
-            )
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await audiobuffer.stop_recording()
-            await task.cancel()
-
-        # Handler for merged audio
-        @audiobuffer.event_handler("on_audio_data")
-        async def on_audio_data(buffer, audio, sample_rate, num_channels):
-            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-            filename = f"recordings/merged_{timestamp}.wav"
-            os.makedirs("recordings", exist_ok=True)
-            await save_audio_file(audio, filename, sample_rate, num_channels)
-
-        # Handler for separate tracks
-        @audiobuffer.event_handler("on_track_audio_data")
-        async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
-            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-            os.makedirs("recordings", exist_ok=True)
-
-            # Save user audio
-            user_filename = f"recordings/user_{timestamp}.wav"
-            await save_audio_file(user_audio, user_filename, sample_rate, 1)
-
-            # Save bot audio
-            bot_filename = f"recordings/bot_{timestamp}.wav"
-            await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/phone-chatbot/bot_daily_gemini.py

@@ -7,15 +7,20 @@ import argparse
 import asyncio
 import os
 import sys
+from dataclasses import dataclass
 from typing import Optional
 
+import google.ai.generativelanguage as glm
 from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
+    BotStoppedSpeakingFrame,
     EndTaskFrame,
+    Frame,
     InputAudioRawFrame,
+    SystemFrame,
     TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
@@ -23,11 +28,11 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.ai_services import LLMService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.google import GoogleLLMService
-from pipecat.services.google.google import GoogleLLMContext
+from pipecat.services.google import GoogleLLMContext, GoogleLLMService
 from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -235,7 +240,7 @@ If it sounds like a human (saying hello, asking questions, etc.), call the funct
 DO NOT say anything until you've determined if this is a voicemail or human."""
 
     llm = GoogleLLMService(
-        model="models/gemini-2.0-flash-lite",
+        model="models/gemini-2.0-flash-lite-preview-02-05",
         api_key=os.getenv("GOOGLE_API_KEY"),
         system_instruction=system_instruction,
         tools=tools,

pyproject.toml

@@ -20,14 +20,17 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence"
 ]
 dependencies = [
-    "aiohttp~=3.11.13",
+    "aiohttp~=3.11.11",
     "audioop-lts~=0.2.1; python_version>='3.13'",
+    # We need an older version of `httpx` that doesn't remove the deprecated
+    # `proxies` argument. This is necessary for Azure and Anthropic clients.
+    "httpx~=0.27.2",
     "loguru~=0.7.3",
     "Markdown~=3.7",
     "numpy~=1.26.4",
     "Pillow~=11.1.0",
     "protobuf~=5.29.3",
-    "pydantic~=2.10.6",
+    "pydantic~=2.10.5",
     "pyloudnorm~=0.1.1",
     "resampy~=0.4.3",
     "soxr~=0.5.0",
@@ -39,7 +42,7 @@ Source = "https://github.com/pipecat-ai/pipecat"
 Website = "https://pipecat.ai"
 
 [project.optional-dependencies]
-anthropic = [ "anthropic~=0.47.2" ]
+anthropic = [ "anthropic~=0.45.2" ]
 assemblyai = [ "assemblyai~=0.36.0" ]
 aws = [ "boto3~=1.35.99" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0"]
@@ -47,13 +50,13 @@ canonical = [ "aiofiles~=24.1.0" ]
 cartesia = [ "cartesia~=1.3.1", "websockets~=13.1" ]
 cerebras = []
 deepseek = []
-daily = [ "daily-python~=0.15.0" ]
+daily = [ "daily-python~=0.14.2" ]
 deepgram = [ "deepgram-sdk~=3.8.0" ]
 elevenlabs = [ "websockets~=13.1" ]
 fal = [ "fal-client~=0.5.6" ]
 fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
 gladia = [ "websockets~=13.1" ]
-google = [ "google-cloud-speech~=2.31.0", "google-cloud-texttospeech~=2.25.0", "google-genai~=1.3.0", "google-generativeai~=0.8.4" ]
+google = [ "google-cloud-speech~=2.31.0", "google-cloud-texttospeech~=2.25.0", "google-genai~=1.2.0", "google-generativeai~=0.8.4" ]
 grok = []
 groq = []
 gstreamer = [ "pygobject~=3.50.0" ]

src/pipecat/processors/audio/audio_buffer_processor.py

@@ -21,32 +21,20 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
 class AudioBufferProcessor(FrameProcessor):
-    """Processes and buffers audio frames from both input (user) and output (bot) sources.
+    """This processor buffers audio raw frames (input and output). The mixed
+    audio can be obtained by registering an "on_audio_data" event handler.
+    The event handler will be called every time `buffer_size` is reached.
 
-    This processor manages audio buffering and synchronization, providing both merged and
-    track-specific audio access through event handlers. It supports various audio configurations
-    including sample rate conversion and mono/stereo output.
+    You can provide the desired output `sample_rate` and incoming audio frames
+    will resampled to match it. Also, you can provide the number of channels, 1
+    for mono and 2 for stereo. With mono audio user and bot audio will be mixed,
+    in the case of stereo the left channel will be used for the user's audio and
+    the right channel for the bot.
 
-    Events:
-        on_audio_data: Triggered when buffer_size is reached, providing merged audio
-        on_track_audio_data: Triggered when buffer_size is reached, providing separate tracks
+    Most of the time, user audio will be a continuous stream but it's possible
+    that in some cases only the spoken audio is sent. To accomodate for those
+    cases make sure to set `user_continuous_stream` accordingly.
 
-    Args:
-        sample_rate (Optional[int]): Desired output sample rate. If None, uses source rate
-        num_channels (int): Number of channels (1 for mono, 2 for stereo). Defaults to 1
-        buffer_size (int): Size of buffer before triggering events. 0 for no buffering
-        user_continuous_stream (bool): Whether user audio is continuous or speech-only
-
-    Audio handling:
-        - Mono output (num_channels=1): User and bot audio are mixed
-        - Stereo output (num_channels=2): User audio on left, bot audio on right
-        - Automatic resampling of incoming audio to match desired sample_rate
-        - Silence insertion for non-continuous audio streams
-        - Buffer synchronization between user and bot audio
-
-    Note:
-        When user_continuous_stream is False, the processor expects only speech
-        segments and will handle silence insertion between segments automatically.
     """
 
     def __init__(
@@ -77,45 +65,21 @@ class AudioBufferProcessor(FrameProcessor):
         self._resampler = create_default_resampler()
 
         self._register_event_handler("on_audio_data")
-        self._register_event_handler("on_track_audio_data")
 
     @property
     def sample_rate(self) -> int:
-        """Current sample rate of the audio processor.
-
-        Returns:
-            int: The sample rate in Hz
-        """
         return self._sample_rate
 
     @property
     def num_channels(self) -> int:
-        """Number of channels in the audio output.
-
-        Returns:
-            int: Number of channels (1 for mono, 2 for stereo)
-        """
         return self._num_channels
 
     def has_audio(self) -> bool:
-        """Check if both user and bot audio buffers contain data.
-
-        Returns:
-            bool: True if both buffers contain audio data
-        """
         return self._buffer_has_audio(self._user_audio_buffer) and self._buffer_has_audio(
             self._bot_audio_buffer
         )
 
     def merge_audio_buffers(self) -> bytes:
-        """Merge user and bot audio buffers into a single audio stream.
-
-        For mono output, audio is mixed. For stereo output, user audio is placed
-        on the left channel and bot audio on the right channel.
-
-        Returns:
-            bytes: Mixed audio data
-        """
         if self._num_channels == 1:
             return mix_audio(bytes(self._user_audio_buffer), bytes(self._bot_audio_buffer))
         elif self._num_channels == 2:
@@ -126,23 +90,14 @@ class AudioBufferProcessor(FrameProcessor):
             return b""
 
     async def start_recording(self):
-        """Start recording audio from both user and bot.
-
-        Initializes recording state and resets audio buffers.
-        """
         self._recording = True
         self._reset_recording()
 
     async def stop_recording(self):
-        """Stop recording and trigger final audio data handlers.
-
-        Calls audio handlers with any remaining buffered audio before stopping.
-        """
         await self._call_on_audio_data_handler()
         self._recording = False
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming audio frames and manage audio buffers."""
         await super().process_frame(frame, direction)
 
         # Update output sample rate if necessary.
@@ -205,21 +160,10 @@ class AudioBufferProcessor(FrameProcessor):
         if not self.has_audio() or not self._recording:
             return
 
-        # Call original handler with merged audio
         merged_audio = self.merge_audio_buffers()
         await self._call_event_handler(
             "on_audio_data", merged_audio, self._sample_rate, self._num_channels
         )
-
-        # Call new handler with separate tracks
-        await self._call_event_handler(
-            "on_track_audio_data",
-            bytes(self._user_audio_buffer),
-            bytes(self._bot_audio_buffer),
-            self._sample_rate,
-            self._num_channels,
-        )
-
         self._reset_audio_buffers()
 
     def _buffer_has_audio(self, buffer: bytearray) -> bool:

src/pipecat/services/elevenlabs.py

@@ -570,12 +570,10 @@ class ElevenLabsHttpTTSService(TTSService):
 
                 await self.start_tts_usage_metrics(text)
 
-                # Process the streaming response
-                CHUNK_SIZE = 1024
-
                 yield TTSStartedFrame()
-                async for chunk in response.content.iter_chunked(CHUNK_SIZE):
-                    if len(chunk) > 0:
+
+                async for chunk in response.content:
+                    if chunk:
                         await self.stop_ttfb_metrics()
                         yield TTSAudioRawFrame(chunk, self.sample_rate, 1)
         except Exception as e:

src/pipecat/services/google/google.py

@@ -722,9 +722,7 @@ class GoogleLLMContext(OpenAILLMContext):
 
         self.add_message(glm.Content(role="user", parts=parts))
 
-    def add_audio_frames_message(
-        self, *, audio_frames: list[AudioRawFrame], text: str = "Audio follows"
-    ):
+    def add_audio_frames_message(self, *, audio_frames: list[AudioRawFrame], text: str = None):
         if not audio_frames:
             return
 
@@ -733,9 +731,8 @@ class GoogleLLMContext(OpenAILLMContext):
 
         parts = []
         data = b"".join(frame.audio for frame in audio_frames)
-        # NOTE(aleix): According to the docs only text or inline_data should be needed.
-        # (see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference)
-        parts.append(glm.Part(text=text))
+        if text:
+            parts.append(glm.Part(text=text))
         parts.append(
             glm.Part(
                 inline_data=glm.Blob(

src/pipecat/services/openai.py

@@ -530,10 +530,8 @@ class OpenAITTSService(TTSService):
 
                 await self.start_tts_usage_metrics(text)
 
-                CHUNK_SIZE = 1024
-
                 yield TTSStartedFrame()
-                async for chunk in r.iter_bytes(CHUNK_SIZE):
+                async for chunk in r.iter_bytes(8192):
                     if len(chunk) > 0:
                         await self.stop_ttfb_metrics()
                         frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)

src/pipecat/services/playht.py

@@ -323,8 +323,7 @@ class PlayHTHttpTTSService(TTSService):
         api_key: str,
         user_id: str,
         voice_url: str,
-        voice_engine: str = "Play3.0-mini",
-        protocol: str = "http",  # Options: http, ws
+        voice_engine: str = "Play3.0-mini-http",  # Options: Play3.0-mini-http, Play3.0-mini-ws
         sample_rate: Optional[int] = None,
         params: InputParams = InputParams(),
         **kwargs,
@@ -338,24 +337,12 @@ class PlayHTHttpTTSService(TTSService):
             user_id=self._user_id,
             api_key=self._api_key,
         )
-
-        # Check if voice_engine contains protocol information (backward compatibility)
-        if "-http" in voice_engine:
-            # Extract the base engine name
-            voice_engine = voice_engine.replace("-http", "")
-            protocol = "http"
-        elif "-ws" in voice_engine:
-            # Extract the base engine name
-            voice_engine = voice_engine.replace("-ws", "")
-            protocol = "ws"
-
         self._settings = {
             "language": self.language_to_service_language(params.language)
             if params.language
             else "english",
             "format": Format.FORMAT_WAV,
             "voice_engine": voice_engine,
-            "protocol": protocol,
             "speed": params.speed,
             "seed": params.seed,
         }
@@ -396,22 +383,19 @@ class PlayHTHttpTTSService(TTSService):
 
         try:
             options = self._create_options()
+            b = bytearray()
+            in_header = True
 
             await self.start_ttfb_metrics()
 
             playht_gen = self._client.tts(
-                text,
-                voice_engine=self._settings["voice_engine"],
-                protocol=self._settings["protocol"],
-                options=options,
+                text, voice_engine=self._settings["voice_engine"], options=options
             )
 
             await self.start_tts_usage_metrics(text)
 
             yield TTSStartedFrame()
 
-            b = bytearray()
-            in_header = True
             async for chunk in playht_gen:
                 # skip the RIFF header.
                 if in_header:
@@ -426,10 +410,11 @@ class PlayHTHttpTTSService(TTSService):
                             fh.read(size)
                             (data, size) = struct.unpack("<4sI", fh.read(8))
                         in_header = False
-                elif len(chunk) > 0:
-                    await self.stop_ttfb_metrics()
-                    frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
-                    yield frame
+                else:
+                    if len(chunk):
+                        await self.stop_ttfb_metrics()
+                        frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
+                        yield frame
         except Exception as e:
             logger.error(f"{self} error generating TTS: {e}")
         finally:

src/pipecat/services/rime.py

@@ -407,10 +407,10 @@ class RimeHttpTTSService(TTSService):
                 yield TTSStartedFrame()
 
                 # Process the streaming response
-                CHUNK_SIZE = 1024
+                chunk_size = 8192
 
-                async for chunk in response.content.iter_chunked(CHUNK_SIZE):
-                    if len(chunk) > 0:
+                async for chunk in response.content.iter_chunked(chunk_size):
+                    if chunk:
                         await self.stop_ttfb_metrics()
                         frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
                         yield frame

src/pipecat/services/xtts.py

@@ -150,10 +150,8 @@ class XTTSService(TTSService):
 
             yield TTSStartedFrame()
 
-            CHUNK_SIZE = 1024
-
             buffer = bytearray()
-            async for chunk in r.content.iter_chunked(CHUNK_SIZE):
+            async for chunk in r.content.iter_chunked(1024):
                 if len(chunk) > 0:
                     await self.stop_ttfb_metrics()
                     # Append new chunk to the buffer.

src/pipecat/transports/base_output.py

@@ -232,9 +232,6 @@ class BaseOutputTransport(FrameProcessor):
             await self.push_frame(BotStoppedSpeakingFrame())
             await self.push_frame(BotStoppedSpeakingFrame(), FrameDirection.UPSTREAM)
             self._bot_speaking = False
-            # Clean audio buffer (there could be tiny left overs if not multiple
-            # to our output chunk size).
-            self._audio_buffer = bytearray()
 
     #
     # Sink tasks