> ## Documentation Index > Fetch the complete documentation index at: https://docs.together.ai/llms.txt > Use this file to discover all available pages before exploring further. # Voice activity detection > Configure voice activity detection to control how speech segments are detected in real-time transcription. Together AI's real-time transcription API uses Voice Activity Detection (VAD) to automatically identify speech segments in an audio stream. While speech is ongoing, the server streams partial transcriptions as `delta` events. When VAD detects enough silence, the segment ends and the server emits a final `completed` event with the full transcript. VAD runs a dedicated model on the server to compute a speech probability for each audio frame. Frames above a configurable threshold are classified as speech, and the resulting speech regions are grouped into segments based on silence gaps, minimum durations, and padding. ## Parameters All VAD parameters are optional. If omitted, the server uses sensible defaults tuned for conversational audio. | Parameter | Type | Default | Description | | ------------------------- | ----- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `threshold` | float | `0.3` | Speech probability threshold (0.0–1.0). Frames above this value are classified as speech. Lower values detect more speech but may increase false positives. | | `min_silence_duration_ms` | int | `500` | How long silence must last (in ms) before a speech segment ends. Higher values prevent splitting on brief pauses. | | `min_speech_duration_ms` | int | `250` | Minimum segment length in ms. Segments shorter than this are discarded, useful for filtering noise bursts. | | `max_speech_duration_s` | float | `5.0` | Maximum segment length in seconds. Longer segments are split at the best internal silence point. | | `speech_pad_ms` | int | `250` | Padding added to the start and end of each segment. Prevents clipping speech edges. Adjacent segments never overlap; if padding would cause overlap, the gap is split at the midpoint. | ## Common configurations ### Conversational audio (default) The defaults work well for typical voice assistant and conversational use cases: clean microphone audio at 16kHz with turn-taking between speakers. ```json theme={null} { "type": "server_vad", "threshold": 0.3, "min_silence_duration_ms": 500, "min_speech_duration_ms": 250, "max_speech_duration_s": 5.0, "speech_pad_ms": 250 } ``` ### Phone calls and low-quality audio Phone audio (8kHz, low SNR) produces lower speech probabilities, so a much lower threshold is needed. Higher `min_silence_duration_ms` prevents splitting mid-sentence pauses common in call center recordings. A higher `max_speech_duration_s` allows longer uninterrupted turns. ```json theme={null} { "type": "server_vad", "threshold": 0.01, "min_silence_duration_ms": 1000, "min_speech_duration_ms": 500, "max_speech_duration_s": 60, "speech_pad_ms": 10 } ``` ## Configure VAD You can configure VAD in two ways: ### Query parameters at connection time Pass VAD parameters directly in the WebSocket URL: ``` wss://api.together.ai/v1/realtime?model=openai/whisper-large-v3&input_audio_format=pcm_s16le_16000&threshold=0.01&min_silence_duration_ms=1000 ``` To disable VAD entirely, use `turn_detection=none`: ``` wss://api.together.ai/v1/realtime?model=openai/whisper-large-v3&input_audio_format=pcm_s16le_16000&turn_detection=none ``` ### Session message after connection Send a `transcription_session.updated` message after receiving `session.created`: ```json theme={null} { "type": "transcription_session.updated", "session": { "turn_detection": { "type": "server_vad", "threshold": 0.01, "min_silence_duration_ms": 1000, "min_speech_duration_ms": 500, "max_speech_duration_s": 60, "speech_pad_ms": 10 } } } ``` To disable VAD via session message, set `turn_detection` to `null`: ```json theme={null} { "type": "transcription_session.updated", "session": { "turn_detection": null } } ``` ## Disable VAD With VAD disabled, the server does not automatically segment audio. No `completed` events are emitted until you explicitly send an `input_audio_buffer.commit` message, at which point the entire buffered audio is transcribed. This is useful when your application controls segmentation externally. ## Example: real-time transcription with custom VAD ```python Python theme={null} import asyncio import base64 import json import os import websockets API_KEY = os.environ["TOGETHER_API_KEY"] MODEL = "openai/whisper-large-v3" VAD_CONFIG = { "type": "server_vad", "threshold": 0.3, "min_silence_duration_ms": 500, "min_speech_duration_ms": 250, "max_speech_duration_s": 5.0, "speech_pad_ms": 250, } async def transcribe(): url = f"wss://api.together.ai/v1/realtime?model={MODEL}&input_audio_format=pcm_s16le_16000" headers = {"Authorization": f"Bearer {API_KEY}"} async with websockets.connect(url, additional_headers=headers) as ws: # Wait for session.created, then send VAD config msg = json.loads(await ws.recv()) if msg["type"] == "session.created": await ws.send( json.dumps( { "type": "transcription_session.updated", "session": {"turn_detection": VAD_CONFIG}, } ) ) # Send audio in 100ms chunks at real-time pace with open("audio.wav", "rb") as f: audio = f.read() CHUNK = 3200 # 100ms at 16kHz 16-bit for i in range(0, len(audio), CHUNK): await ws.send( json.dumps( { "type": "input_audio_buffer.append", "audio": base64.b64encode( audio[i : i + CHUNK] ).decode(), } ) ) await asyncio.sleep(0.1) await ws.send(json.dumps({"type": "input_audio_buffer.commit"})) # Receive transcription results async for message in ws: data = json.loads(message) if ( data["type"] == "conversation.item.input_audio_transcription.completed" ): print(data["transcript"]) elif ( data["type"] == "conversation.item.input_audio_transcription.failed" ): print(f"Error: {data['error']['message']}") break asyncio.run(transcribe()) ``` ```javascript JavaScript theme={null} import WebSocket from "ws"; import fs from "fs"; const API_KEY = process.env.TOGETHER_API_KEY; const MODEL = "openai/whisper-large-v3"; const VAD_CONFIG = { type: "server_vad", threshold: 0.3, min_silence_duration_ms: 500, min_speech_duration_ms: 250, max_speech_duration_s: 5.0, speech_pad_ms: 250, }; const url = `wss://api.together.ai/v1/realtime?model=${MODEL}&input_audio_format=pcm_s16le_16000`; const ws = new WebSocket(url, { headers: { Authorization: `Bearer ${API_KEY}` }, }); ws.on("open", () => console.log("Connected")); ws.on("message", (raw) => { const data = JSON.parse(raw); if (data.type === "session.created") { // Send VAD config ws.send(JSON.stringify({ type: "transcription_session.updated", session: { turn_detection: VAD_CONFIG }, })); // Send audio in 100ms chunks at real-time pace const audio = fs.readFileSync("audio.wav"); const CHUNK = 3200; // 100ms at 16kHz 16-bit let i = 0; const interval = setInterval(() => { if (i < audio.length) { ws.send(JSON.stringify({ type: "input_audio_buffer.append", audio: audio.subarray(i, i + CHUNK).toString("base64"), })); i += CHUNK; } else { clearInterval(interval); ws.send(JSON.stringify({ type: "input_audio_buffer.commit" })); } }, 100); } if (data.type === "conversation.item.input_audio_transcription.completed") { console.log(data.transcript); } if (data.type === "conversation.item.input_audio_transcription.failed") { console.error("Error:", data.error.message); ws.close(); } }); ``` ## Next steps * See [Streaming transcription](/docs/inference/transcription/streaming) for the full real-time streaming guide. * See the [API reference](/reference/audio-transcriptions-realtime) for the complete WebSocket endpoint specification.