How would you implement a mechanism to detect answering machines? Is there any built-in mechanism in LiveKit?

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

I'd like to combine a silence solution with a LLM based approach. How would you integrate in the LiveKit pipeline without adding delays in the responses?

Thanks — I’m responding to your question about combining silence detection with an LLM approach in LiveKit while avoiding added response latency. High‑level approach • Do real‑time silence detection in the media path to make immediate, low‑latency decisions. • Parallelize LLM transcription/analysis off the media path so it runs asynchronously and only informs or overrides the real‑time decision when available. • Combine the two decisions with simple rules so you can act quickly on silence but refine behavior when LLM results arrive. Architecture (concise) • RTP/LiveKit pipeline: continue normal audio forwarding and speech activity events. • Real‑time detector: run a lightweight VAD/silence monitor in process (or a fast worker) that tracks

last_spoke_time

and triggers a short‑timeout action (e.g., hold, play prompt, hang up) if silence > threshold. • Async analyzer: simultaneously capture a short initial audio buffer (e.g., first 5–15s) and stream it to a transcription service. When transcription is ready, send text to the LLM classifier to detect voicemail/answering‑machine cues. • Decision reconciler: when LLM result returns, merge with silence decision: ◦ If real‑time silence already triggered an action, allow a cancel/override if LLM indicates a human (to avoid false positives). ◦ If silence triggered nothing yet, LLM can proactively mark voicemail and perform the chosen action. Implementation notes / best practices • Keep immediate path minimal: ◦ Use VAD or last‑spoken timestamp and a short threshold (3–7s) for quick action. ◦ Do not block on transcription or LLM calls in the media thread. • Capture audio non‑intrusively: ◦ Duplicate the incoming audio stream to a short circular buffer (5–15s). This buffer feeds the async transcription without affecting live forwarding. ◦ Use WebRTC/LiveKit APIs to subscribe to the participant’s audio and copy frames to the buffer in a separate worker/goroutine/task. • Transcription/LLM pipeline: ◦ Stream audio to a low‑latency speech‑to‑text service (or run a local model) to reduce turnaround. ◦ Send the earliest transcription segment(s) to the LLM classifier rather than waiting for full audio. ◦ Keep the LLM prompt focused and small—binary classify “voicemail vs human” with confidence score. • Use confidence thresholds and hysteresis: ◦ Require both long silence and a voicemail classification (or allow one to suffice depending on risk tolerance). ◦ If LLM confidence is low, prefer to keep the call open or play a clarifying prompt. • Actions and UX: ◦ For false positive safety, prefer non‑destructive actions first (play a short prompt asking user to press a key) before hanging up. ◦ If you auto‑hang, allow a small grace period where LLM can override. • Performance and cost: ◦ Batch or stream transcriptions to reduce latency and cost. ◦ Cache LLM decisions for similar greeting patterns in your call set. Concrete flow (step sequence) 1. On call connect, start VAD and start filling a 10s circular audio buffer. 2. If

now - last_spoke_time > SILENCE_THRESHOLD

(3–7s), take the quick action (e.g., mark probable voicemail and play a prompt or hang up). 3. Meanwhile, stream the buffered audio to STT; as soon as the first segment is ready, call the LLM classifier. 4. When LLM result returns: ◦ If it indicates human and you already took a destructive action, cancel/undo if possible (e.g., rejoin or alert agent). ◦ Otherwise, apply/confirm the voicemail decision (log, metrics, or hang up). 1. Record decisions and confidence for tuning. If you’d like, I can: • Provide a sample code sketch for capturing audio from LiveKit and populating a circular buffer. • Show an example LLM prompt for voicemail classification and thresholds to use. Which would you prefer first?, with interactive elements

Yes, provide a sample code sketch for capturing audio

Thanks — I’ll show a concise Python sketch that captures audio from LiveKit, writes to a circular buffer for async transcription/LLM processing, and runs real‑time silence detection without blocking the media path. This sketch assumes you have a LiveKit participant audio consumer hook (or server SDK callback) that gives you raw audio frames (PCM). If you need a different language (Go) or your SDK provides a different frame format, tell me and I’ll adapt it. Key pieces: • A thread-safe circular buffer for recent audio (5–15s). • A non-blocking VAD/silence monitor using last_spoke_time. • A background worker that drains the buffer and sends audio to STT → LLM async.

import time
import threading
import collections
import numpy as np
import soundfile as sf  # pip install soundfile for WAV encoding
# Placeholder for your STT/LLM client
# from my_stt_client import async_transcribe
# from my_llm_client import classify_voicemail

SAMPLE_RATE = 48000
CHANNELS = 1
BUFFER_SECONDS = 12
SILENCE_THRESHOLD = 5.0  # seconds

# Circular buffer storing PCM int16 frames
class CircularAudioBuffer:
    def __init__(self, max_seconds, sample_rate):
        self.max_samples = int(max_seconds * sample_rate)
        self.buf = collections.deque(maxlen=self.max_samples)
        self.lock = threading.Lock()

    def write_frames(self, samples: np.ndarray):
        with self.lock:
            self.buf.extend(samples.tolist())

    def read_all(self):
        with self.lock:
            data = np.array(self.buf, dtype=np.int16)
            return data

    def clear(self):
        with self.lock:
            self.buf.clear()

# Global state for example
audio_buffer = CircularAudioBuffer(BUFFER_SECONDS, SAMPLE_RATE)
last_spoke_time = time.time()
in_call = True

# This function should be called by the LiveKit audio callback whenever you receive audio frames
def on_audio_frame(pcm_int16_bytes: bytes):
    # Convert raw bytes to int16 numpy array (mono)
    samples = np.frombuffer(pcm_int16_bytes, dtype=np.int16)
    audio_buffer.write_frames(samples)

    # Update last spoke time using simple energy check (fast VAD)
    # Compute RMS energy quickly
    rms = np.sqrt(np.mean(samples.astype(np.float32) ** 2) + 1e-8)
    ENERGY_THRESHOLD = 500  # tune this for your audio level
    global last_spoke_time
    if rms > ENERGY_THRESHOLD:
        last_spoke_time = time.time()

# Lightweight real-time monitor (non-blocking)
def silence_monitor_loop():
    global in_call
    while in_call:
        now = time.time()
        if now - last_spoke_time > SILENCE_THRESHOLD:
            # Quick real-time action: mark probable voicemail (non-destructive by default)
            print("[silence_monitor] Silence threshold exceeded — probable voicemail. Trigger quick action.")
            # Example quick action: play prompt or flag; do NOT block here.
            # You can spawn a task to confirm with LLM result or to perform destructive action.
            # After triggering once, wait longer to avoid repeated triggers
            time.sleep(2)
        time.sleep(0.1)

# Async worker: periodically send buffered audio to STT then LLM classifier
def async_analysis_worker():
    global in_call
    while in_call:
        # Wait a short time to collect initial audio (e.g., every 2s)
        time.sleep(2.0)

        wav_samples = audio_buffer.read_all()
        if wav_samples.size == 0:
            continue

        # Encode to WAV in-memory for STT if needed
        wav_bytes = None
        with sf.SoundFile('temp.wav', mode='w', samplerate=SAMPLE_RATE, channels=CHANNELS, subtype='PCM_16') as f:
            f.write(wav_samples.reshape(-1, CHANNELS))
        with open('temp.wav', 'rb') as f:
            wav_bytes = f.read()

        # Kick off async transcription (non-blocking). Replace with your async client.
        # transcription = await async_transcribe(wav_bytes)
        # For sketch: simulate
        transcription = fake_transcribe(wav_bytes)

        # Call LLM classifier (non-blocking)
        # llm_result = await classify_voicemail(transcription)
        llm_result = fake_classify(transcription)

        # Decide: combine silence and llm_result
        now = time.time()
        silence = (now - last_spoke_time) > SILENCE_THRESHOLD
        if llm_result['prob_voicemail'] > 0.85 or (silence and llm_result['prob_voicemail'] > 0.6):
            print("[async_analysis] Confirmed voicemail (confidence=%s). Taking action." % llm_result['prob_voicemail'])
            # Take your action (e.g., hang up, record message). Destructive actions should consider a short grace period to allow override.
        else:
            print("[async_analysis] Likely human (confidence=%s). No hangup." % llm_result['prob_voicemail'])

        # Optionally clear buffer after successful analysis to save memory
        # audio_buffer.clear()

# Fake helper functions for sketch
def fake_transcribe(wav_bytes):
    # return simulated transcription text quickly
    return "Hello, you have reached the voicemail. Leave a message after the beep."

def fake_classify(transcription_text):
    # very basic keyword check to simulate
    keywords = ["leave a message", "beep", "voicemail"]
    score = 0.0
    for kw in keywords:
        if kw in transcription_text.lower():
            score += 0.5
    score = min(score, 0.99)
    return {"prob_voicemail": score}

# Start threads (in your server init)
monitor_thread = threading.Thread(target=silence_monitor_loop, daemon=True)
analyze_thread = threading.Thread(target=async_analysis_worker, daemon=True)
monitor_thread.start()
analyze_thread.start()

# Example: simulate incoming audio frames from LiveKit
def simulate_incoming_audio():
    # Generate silence frames then a greeting
    silence_frames = (np.zeros(int(SAMPLE_RATE * 6), dtype=np.int16)).tobytes()
    greeting_frames = (np.random.randint(-1000, 1000, size=int(SAMPLE_RATE * 3)).astype(np.int16)).tobytes()
    on_audio_frame(silence_frames)
    time.sleep(1)
    on_audio_frame(greeting_frames)

# Call simulate_incoming_audio() manually in tests

Notes and tuning tips • Energy threshold and sample rate must match your audio source—tune

ENERGY_THRESHOLD

. • Use an efficient in-memory WAV encoder or raw PCM streaming to your STT to reduce disk I/O; the example writes a temp file for simplicity. • Use streaming STT to reduce latency: send the buffer progressively and call the LLM as soon as the first partial transcript arrives. • Keep the immediate media callback minimal: just write frames and update

last_spoke_time

. All heavier work must be in background threads or async tasks. • For production, use robust thread-safe ring buffers and avoid disk writes. If you want, I can: • Convert this sketch to Go (server SDK). • Replace the fake STT/LLM with code for a recommended low‑latency STT provider and a sample LLM prompt for classification. Which would you like next?, with interactive elements

Can I get the STT transcritption instead of the audio? I'd need the transcription from the beginning although interruptions are disabled

Thanks — I’m responding to your request about getting STT transcription from the very start of the call (instead of raw audio), even when interruptions are disabled. Short answer • Yes — you can receive transcription (interim + final) from the start via LiveKit’s transcription text stream (

lk.transcription

). Interruption settings do not prevent transcription from being produced. How to integrate (concise) 1. Register a text stream handler for

lk.transcription

in your room/agent. 2. Consume interim messages as they arrive, and replace them with the final message when

lk.transcription_final

is true. 3. Use those early interim transcripts for LLM classification while continuing silence detection in the media path. Do not block on final transcripts — accept partial results to avoid latency. Minimal TypeScript sketch

// register once when your agent/room starts
room.registerTextStreamHandler('lk.transcription', async (reader, participantInfo) => {
  // read messages as they arrive (includes interim and final)
  for await (const msg of reader) {
    const text = msg.data; // transcription text chunk
    const isFinal = reader.info.attributes['lk.transcription_final'] === 'true';

    // Forward interim text immediately to your async LLM classifier (non-blocking)
    processTranscriptionChunk(participantInfo.participantSid, text, isFinal);
  }
});

async function processTranscriptionChunk(participantSid: string, text: string, isFinal: boolean) {
  // 1) Feed partial transcript to an LLM classifier for quick voicemail/human scoring
  // 2) Merge LLM score with silence detector state (last_spoke_time)
  // 3) If classifier strongly indicates voicemail, take your action (or flag for confirm)
  // Note: keep this non-blocking so you don't delay live responses
}

Design tips to avoid added latency • Use interim transcripts immediately; do not wait for final. • Run transcription→LLM classification asynchronously off the media thread. • Combine quick VAD/silence logic (3–7s) with incremental LLM scores; require a confidence threshold to act destructively. • If you need guaranteed “from the very first word”, subscribe before connecting the participant so you start receiving interim chunks immediately at call start. If you want, I can: • Provide a Go sketch or a concrete LLM prompt for voicemail classification. • Show how to merge interim transcript confidence with your silence monitor. Which would you like next? Sources: Sequential Pipeline Architecture for Voice Agents | LiveKit | Text and transcriptions | LiveKit Documentation | Turns overview | LiveKit Documentation, with interactive elements

would VAD + lk.

transcription

(instead of audio stream) be a god decision?

Short answer: Yes — combining VAD (real‑time silence/energy detection) with LiveKit’s

lk.transcription

is a good decision. It gives fast, low‑latency signals from VAD plus semantic evidence from STT/LLM without needing to process raw audio in your pipeline. Why this is a good approach • Low latency: VAD provides immediate signals (last_spoke_time) for quick decisions (3–7s) so you can act without waiting for STT. • Richer accuracy:

lk.transcription

supplies interim transcripts from the very start so you can run lightweight LLM/classification on text to confirm voicemail patterns and reduce false positives. • Lighter pipeline: Processing text is typically cheaper and simpler than streaming raw audio to STT/LLM yourself, and avoids heavy media processing on the critical media path. • Non-blocking: You can make real‑time actions from VAD and let the transcription/LLM run asynchronously to confirm or override. Trade-offs and pitfalls • STT errors: Interim transcripts can be noisy; use confidence thresholds and prefer conservative destructive actions. • Timing mismatch: Interim transcripts arrive after some audio; ensure your logic tolerates a small lag (use VAD for immediate decisions). • Partial context: Short transcripts may be ambiguous—combine text score with silence and require combined confidence for hangups. • Cost/latency of LLMs: Use small targeted prompts or classifiers for low latency; stream partial transcripts to the LLM as they arrive. Practical recommendations • Keep the media callback minimal: only update

last_spoke_time

(VAD) and register

lk.transcription

handler to receive interim/final text. • Use interim transcripts immediately and call LLM/classifier asynchronously (don’t block the room thread). • Use combined rules, for example: ◦ Quick flag if silence > 5s. ◦ Confirm voicemail if (silence AND transcription_score > 0.6) OR (transcription_score > 0.9). • Prefer non‑destructive first actions (play prompt, pause, record) and only hang up when confidence is high. • Tune thresholds with real call samples and log decisions to iterate. • If you need near‑real‑time text, subscribe to

lk.transcription

before connecting the participant so interim chunks start from the first audio. Tiny code outline (concept) 1. VAD updates

last_spoke_time

in audio callback. 2. Register

lk.transcription

handler; forward interim text to async classifier. 3. Decision logic (async): ◦ silence = now - last_spoke_time > SILENCE_THRESHOLD ◦ text_score = classifier(interim_text) ◦ if text_score high OR (silence AND moderate_text_score) → take action If you want, I can produce: • A Go or TypeScript snippet that wires

lk.transcription

+ VAD together with the exact decision thresholds you prefer. Which language?, with interactive elements