1. For streaming TTS with SynthesizeStream, what's the correct pattern for pushing audio chunks? Should we call start_segment() or is there a different method? 2. What's the difference between ChunkedStream and SynthesizeStream? When should we use each? 3. For the _run() method in SynthesizeStream, what methods should we call on the output_emitter to properly emit audio? Our specific error is: RuntimeError: start_segment() must be called before pushing audio data But when we call start_segment(segment_id=...), we get: TypeError: start_segment() missing 1 required keyword-only argument: 'segment_id'

Thanks for reaching out, Andrew. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

1. Audio Format and Encoding - What audio format does LiveKit expect from TTS? We're receiving raw PCM from Baseten - do we need to specify format/encoding when pushing chunks? - Our Baseten returns 24kHz, mono, raw PCM - is this correct for push(chunk)? 2. Initialize Parameters - In our output_emitter.initialize() we're passing mime_type="audio/raw". Is this correct for raw PCM? Should it be something else like "audio/pcm"? 3. Error Handling in Streaming - How should we handle errors mid-stream? If Baseten fails partway through streaming, what's the proper way to signal an error to LiveKit? 4. Buffering and Chunk Size - We're using 4096 byte chunks from Baseten. Is there an optimal chunk size for LiveKit? Should we buffer smaller chunks before pushing? 5. Text Buffering Logic - We're buffering text until we hit punctuation or 100 chars. Is this the right approach, or should we send text immediately to reduce latency? 6. The _FlushSentinel Pattern - We see self._FlushSentinel in the stream handling - when/how is this triggered? Should we handle it differently?

Hey there! I've got the answers to your questions, and I'm excited to break them down for you in a friendly way. 1. Audio Format and Encoding: • LiveKit is looking for raw PCM audio with these specs: ◦ Format: Raw PCM ◦ Sample Rate: 24kHz (24000 Hz) ◦ Channels: Mono (1 channel) ◦ Bit Depth: 16-bit • Good news – your Baseten output of 24kHz, mono, raw PCM is spot on for

push(chunk)

! 2. Initialize Parameters: • For

output_emitter.initialize()

,

mime_type="audio/pcm"

is totally the way to go. While

"audio/raw"

works too,

"audio/pcm"

is the standard that everyone uses. 3. Error Handling in Streaming: • If something goes wrong mid-stream, you'll want to use the

_emit_error

method to signal a

TTSError

event. It'll give all the juicy details like error type, when it happened, and if it's something that can be fixed. • For errors that can be recovered, LiveKit's got your back with automatic retries. For the trickier ones, you can set up custom error handling, like playing a backup audio message. • If you're using a FallbackAdapter, it'll automatically switch to a backup TTS provider if the main one stumbles, as long as no audio has been pushed for the current segment. 4. Buffering and Chunk Size: • There's no hard and fast rule for chunk size with LiveKit TTS, but a good thumb rule is to keep chunks under 15kB and aim for 50-250ms of audio for smooth playback. • Buffering smaller chunks isn't a must, but it might help keep your audio output nice and consistent. 5. Text Buffering Logic: • For most apps, it's best to buffer text until you hit a punctuation mark or finish a sentence. This helps keep the speech sounding natural and avoids choppy audio. • Sending text right away can speed things up, but it might make the speech sound a bit robotic. Only go this route if your app is super time-sensitive. 6. The _FlushSentinel Pattern: • The

_FlushSentinel

is like a signal flag for segment boundaries in streaming TTS. It kicks in when you call the

flush()

method. • It makes sure all the text in a segment is processed before moving to the next one. It's crucial for managing resources and keeping everything in sync. • Different TTS systems handle it by flushing or resetting things like tokenizer streams when the sentinel shows up. Need more info or just want to chat about it? I'm here for you! Feel free to ask anything. Sources: livekit-agents/livekit/agents/tts/tts.py | livekit-plugins/livekit-plugins-inworld/livekit/plugins/inworld/tts.py | livekit-plugins/livekit-plugins-google/livekit/plugins/google/beta/gemini_t... | Events and error handling | LiveKit Docs | Resolving "InvalidState - failed to capture frame" errors in AudioSource

In LiveKit Agents Python SDK, what event should I listen for to detect when a user's microphone is connected and they're ready to interact? I want to greet the user only after their microphone permission is granted and audio track is published, not immediately when the agent starts. What's the correct event to use - is it track_published, participant_connected, or something else?

Hey there! Let me help you understand how to detect when a user's microphone is connected and their audio track is published in the LiveKit Agents Python SDK. It's super simple – just listen for the

"track_published"

event. Here's a friendly walkthrough of how to implement it:

@room.on("track_published")
def on_track_published(
    publication: rtc.RemoteTrackPublication, 
    participant: rtc.RemoteParticipant
):
    # Check if it's an audio track from a microphone
    if (publication.kind == rtc.TrackKind.KIND_AUDIO and 
        publication.source == rtc.TrackSource.SOURCE_MICROPHONE):
        <http://logger.info|logger.info>(
            "Microphone track published: %s from participant %s (%s)",
            publication.sid,
            participant.sid,
            participant.identity,
        )
        # Handle the microphone track publication here

Let me break down the key points for you: • The

"track_published"

event is triggered when a remote participant publishes a track. • To make sure it's a microphone audio track, you'll want to check: ◦

publication.kind == rtc.TrackKind.KIND_AUDIO

◦

publication.source == rtc.TrackSource.SOURCE_MICROPHONE

Pro tip: If you need to dive deeper and process the audio data, keep an eye out for the

"track_subscribed"

event. That's when you successfully subscribe to the track. Need any more help? I'm here for you! Sources: livekit-agents/livekit/agents/voice/room_io/_input.py | livekit-rtc/livekit/rtc/room.py

Issue: TTS Segment Mismatch Error Error Message: APIError: number of segments mismatch: expected 1, but got 6 Context: - Using custom BasetenTTS implementation that streams audio from a third-party API - When generate_reply() is called, LiveKit expects 1 segment but our TTS creates multiple (3-6 segments) - This worked before but broke after recent changes Current Implementation Details: - We buffer text and process it in chunks (lines 186-191 in custom_baseten_tts_v2.py) - We create a new segment for each text chunk processed - Segment flow: start_segment() → push() audio chunks → end_segment() → flush() Specific Questions: 1. Single Segment Requirement: When using session.generate_reply(), does LiveKit require exactly ONE segment for the entire response, regardless of text length? 2. Text Buffering: Should we buffer ALL text from _input_ch until we get a _FlushSentinel, then create ONE segment for all accumulated text? 3. Segment ID: Does the segment ID format matter? We use {request_id}-seg-{counter} 4. Streaming vs Buffering: Can we stream audio chunks within a single segment as they arrive from our API, or must we buffer all audio before creating the segment? 5. Flush Timing: When should we call output_emitter.flush()? After each segment or only at the very end? 6. Error Recovery: If we're in the middle of a segment and get an error, what's the proper cleanup sequence? Current Code Structure: async for item in self._input_ch: if isinstance(item, self._FlushSentinel): # Process buffered text - creates segment await process_text(buffered_text) else: # Buffer text until we have enough buffered_text += item if len(buffered_text) > 100 or ends_with_punctuation: # This creates ANOTHER segment! await process_text(buffered_text) What We Need: Clear guidance on whether we should: - A) Create ONE segment at the start and stream all audio into it - B) Buffer ALL text first, then create one segment - C) Some other approach?

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

Question about TTS Streaming and Segments: In our custom TTS that inherits from tts.TTS, we implement the stream() method which returns a SynthesizeStream. Our implementation: 1. Receives text through _input_ch 2. When we get a _FlushSentinel, we know all text for this synthesis request has been received 3. We make an API call to our TTS service with all the buffered text 4. As audio chunks arrive from our API, we: - Call output_emitter.start_segment(segment_id) - Stream chunks with output_emitter.push(chunk) - Call output_emitter.end_segment() - Call output_emitter.flush() The specific question is: When session.generate_reply() is called and text is sent to our TTS stream, should we: - Create exactly ONE segment for all the text received before _FlushSentinel? - Or can we create multiple segments if the text is long? Currently we get "number of segments mismatch: expected 1, but got 5" error when creating multiple segments. What's the correct pattern for segment handling in a streaming TTS implementation? Here are the relevant code snippets from our current implementation: Current TTS Stream Implementation (custom_baseten_tts_v2.py): class BasetenSynthesizeStream(tts.SynthesizeStream): async def _run(self, output_emitter) -> None: """Main streaming loop.""" request_id = f"stream-{uuid.uuid4()}" buffered_text = "" segment_counter = 0 # Track segment numbers for unique IDs # Initialize the output emitter with streaming enabled output_emitter.initialize( request_id=request_id, sample_rate=24000, num_channels=1, mime_type="audio/pcm", stream=True ) # Get session from TTS instance session = await self._tts_instance._ensure_session() async def process_text(text: str) -> None: if not text: return # ... payload creation ... try: async with session.post(self._endpoint, json=payload, ...) as resp: # Start segment before pushing any audio async with self._segment_lock: if self._segment_active: output_emitter.end_segment() self._segment_active = False nonlocal segment_counter segment_counter += 1 segment_id = f"{request_id}-seg-{segment_counter}" output_emitter.start_segment(segment_id=segment_id) self._segment_active = True # Stream audio chunks async for chunk in resp.content.iter_chunked(4096): # Amplify and push audio output_emitter.push(amplified_chunk) # End segment when complete async with self._segment_lock: if self._segment_active: output_emitter.end_segment() self._segment_active = False # Flush to signal completion output_emitter.flush() # Process incoming text - buffer everything until flush try: async for item in self._input_ch: if isinstance(item, self._FlushSentinel): # Process ALL buffered text as ONE segment on flush if buffered_text: segment_counter = 0 # Reset to ensure only one segment await process_text(buffered_text) buffered_text = "" else: # Just buffer text - don't process until flush buffered_text += item The Error We Get: APIError: number of segments mismatch: expected 1, but got 5 How generate_reply is called (voice_agent.py): # Generate initial greeting using generate_reply greeting_instruction = "Greet the user and introduce yourself as Briseis..." await session.generate_reply(instructions=greeting_instruction)