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Access to Echo Omni is provided through Zero Runtime. Visit https://zeroruntime.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface to get started.

Echo Omni hears what was said and how it was said, at the same time.
A verdict in under 80 ms, faster than the pause it is judging.

Most turn detectors wait for silence and hope. A 500 ms gap looks identical whether someone finished a sentence, paused to think, or simply took a breath, so agents built on timers end up talking over people or leaving them hanging.

Echo Omni does not guess. It reads the user's audio together with the transcript of that same turn, so it catches the meaning of the words and the sound of the delivery at once: the trailing pitch, the hesitation, the clipped "mm-hm" that was never a turn at all. One prediction per turn, with a confidence score, fast enough to live inside a real conversation.

It is the flagship of the Echo family, and the broadest of the three: 27 languages, four conversational states, and accuracy that holds across every one of them.

🎯 What it does

Echo Omni classifies every user turn into one of four states. Each state tells the agent exactly what to do next.

State	What it means	What your agent should do
Complete	The user has finished their turn	Hand the turn to the LLM and respond
Incomplete	The user is mid-sentence, just pausing	Keep listening. Do not take the floor
Backchannel	A short acknowledgement: "uh-huh", "okay okay"	Keep speaking. This is not an interruption
Wait	The user is asking you to hold: "wait a minute", "hold on"	Stop speaking immediately

Most turn detectors only answer the first two. Backchannel and Wait are the states that make an agent feel polite instead of oblivious: not stopping every time someone says "mm-hm", and stopping the instant someone says "hold on".

🔌 Input and output

Echo Omni is multimodal, and it needs both signals at once.

Input, per turn:

Field	Description
Audio	The speech segment for the turn
Transcript	The finalized transcript of that same segment
Language	One of the 27 supported codes

Output, one prediction per turn:

Field	Description
State	`Complete`, `Incomplete`, `Backchannel` or `Wait`
Confidence	A score for the prediction, so you can tune how decisive your agent is

Both the audio and the transcript are required, and they must describe the same turn. Text alone cannot tell a thinking pause from a finished thought, and audio alone cannot tell you what was actually said. Echo Omni is built to use both together, which is where its accuracy comes from.

🌍 Supported languages

27 languages, spanning Indian, European and East Asian language families.

Code	Language	Code	Language	Code	Language
`ar`	🇸🇦 Arabic	`bn`	🇧🇩 Bengali	`da`	🇩🇰 Danish
`de`	🇩🇪 German	`en`	🇺🇸 English	`es`	🇪🇸 Spanish
`fi`	🇫🇮 Finnish	`fr`	🇫🇷 French	`gu`	🇮🇳 Gujarati
`hi`	🇮🇳 Hindi	`id`	🇮🇩 Indonesian	`it`	🇮🇹 Italian
`ja`	🇯🇵 Japanese	`ko`	🇰🇷 Korean	`mr`	🇮🇳 Marathi
`nl`	🇳🇱 Dutch	`no`	🇳🇴 Norwegian	`pl`	🇵🇱 Polish
`pt`	🇵🇹 Portuguese	`ru`	🇷🇺 Russian	`ta`	🇮🇳 Tamil
`te`	🇮🇳 Telugu	`tr`	🇹🇷 Turkish	`uk`	🇺🇦 Ukrainian
`ur`	Urdu	`vi`	🇻🇳 Vietnamese	`zh`	🇨🇳 Chinese

This is not a model that works well in English and degrades everywhere else. Accuracy holds across the full set.

📊 Performance

Measured on a held-out multilingual test set of 25,000+ utterances covering all four labels.

Metric	Echo Omni
Accuracy	95.39%
Macro F1	0.9712
F1 Score (Complete)	0.9508

Accuracy holds up across all 27 supported languages, with a mean macro-F1 of 0.9712.

The macro F1 is the number worth looking at. It weights all four states equally, including Backchannel and Wait, which are rare in real traffic and are exactly where lesser turn detectors quietly fall apart. Echo Omni does not trade those away to flatter its headline accuracy.

Results are measured on the benchmark described above. Performance may vary depending on language, deployment configuration, user behaviour and application requirements.

🧠 Trained on data built for this problem

Echo Omni is trained on a proprietary, closed-source dataset built in-house, purpose-made for conversational turn-taking across all 27 languages and all four states, with real acoustic variety rather than clean read speech.

That dataset is the reason the rare states hold up. Backchannels and hold requests barely appear in off-the-shelf speech corpora, so a model trained on public data has almost nothing to learn them from.

🎛️ The Echo family

Echo Omni is the multimodal flagship. Two text-only siblings cover the rest of the latency and accuracy curve, and all three return the same four states, so you can move between them without changing your agent logic.

Model	Modality	Latency	Best for
`echo-small`	Transcript	5 to 10 ms	The lowest latency. The default when responsiveness matters most
`echo-large`	Transcript	10 to 20 ms	Higher accuracy, when it matters more than raw speed
`echo-omni`	Audio + transcript	60 to 80 ms	The widest language coverage and acoustic understanding on top of the transcript

🚀 Get access

Echo Omni is served for you. There is nothing to download, host, or keep running. Plug it straight into your voice pipeline alongside your existing STT, LLM and TTS.

# Set ZERORUNTIME_AUTH_TOKEN in your environment.

from zeroruntime.inference import TurnDetector

# Multimodal: audio and transcript together, 27 languages
turn_detector = TurnDetector(model="echo-omni")

_{Echo Omni by Zero Runtime}

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Collection including zero-runtime/echo-omni

Echo TurnDetection

Collection

Echo is a series of End-of-Turn models built to make voice agents respond at exactly the right moment. • 3 items • Updated about 14 hours ago • 1