---
language:
  - ar
  - bn
  - da
  - de
  - en
  - es
  - fi
  - fr
  - gu
  - hi
  - id
  - it
  - ja
  - ko
  - mr
  - nl
  - no
  - pl
  - pt
  - ru
  - ta
  - te
  - tr
  - uk
  - ur
  - vi
  - zh
pipeline_tag: audio-classification
tags:
  - turn-detection
  - end-of-utterance
  - eou
  - endpointing
  - turn-taking
  - interruption-detection
  - barge-in
  - voice-activity-detection
  - conversational-ai
  - voice-agents
  - real-time
  - low-latency
  - multimodal
  - multilingual
  - audio
  - speech
metrics:
  - recall
  - accuracy
  - f1
model-index:
  - name: echo-omni
    results:
      - task:
          type: audio-classification
          name: End-of-turn detection
        dataset:
          type: mundo-ai/turn-benchmark-test
          name: TurnBench (test)
          config: default
          split: test
        metrics:
          - type: recall
            value: 0.839
            name: End-of-turn recall
          - type: fpr
            value: 0.071
            name: End-of-turn false-positive rate
        source:
          name: TurnBench Leaderboard
          url: https://turnbench.sesame.com/models/echo-omni
      - task:
          type: audio-classification
          name: Interruption detection
        dataset:
          type: mundo-ai/turn-benchmark-test
          name: TurnBench (test)
          config: default
          split: test
        metrics:
          - type: recall
            value: 0.904
            name: Interruption recall
          - type: fpr
            value: 0.129
            name: Interruption false-positive rate
        source:
          name: TurnBench Leaderboard
          url: https://turnbench.sesame.com/models/echo-omni
extra_gated_prompt: "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."
---

<div align="center">

![Echo Omni. 95.39% accuracy, 27 languages, 4 states, verdict under 80 ms.](banner.svg)

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

<p>
<a href="https://zeroruntime.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface"><img src="https://img.shields.io/badge/Get%20Access-zeroruntime.ai-2563EB?style=for-the-badge" alt="Get Access"></a>
&nbsp;
<a href="https://docs.zeroruntime.ai/build/turn-detection-and-interruptions/turn-detection?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface#turn-detection"><img src="https://img.shields.io/badge/Read%20the%20Docs-Turn%20Detection-0F2143?style=for-the-badge" alt="Documentation"></a>
</p>

<p>🏆 <b>Ranked 4th of 18 on end-of-turn and 4th of 16 on interruption</b> on <a href="https://turnbench.sesame.com/models/echo-omni">TurnBench</a>, Sesame AI Labs' public turn-taking benchmark.</p>

</div>

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. It is also independently ranked on [TurnBench](https://turnbench.sesame.com/models/echo-omni), scored blind against a held-out test set.

---

## 🎯 What it does

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

![Every turn resolves to one of four states: Incomplete, Complete, Backchannel or Wait, each with the action the agent should take.](states.svg)

| 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.**

![Audio and the transcript of the same turn go in together, and one classified state comes back.](how-it-works.svg)

**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.

---

## 🏆 TurnBench

[TurnBench](https://turnbench.sesame.com/) is Sesame AI Labs' public benchmark for conversational turn-taking. Systems are scored blind against a held-out test set they never see. We submitted Echo Omni on **10 September 2026**.

| Task | Rank | Recall | False-positive rate |
|:--|:--:|--:|--:|
| **End-of-turn** | **4th of 18** | **0.839** | **0.071** |
| **Interruption** | **4th of 16** | **0.904** | 0.129 |

<sub>Ranks as of 11 September 2026. TurnBench is a live board, so positions move as new systems are submitted; the recall and false-positive figures are fixed properties of the predictions submitted on 10 September 2026.</sub>

Echo Omni is one of only three systems on the board to place top five on **both** tasks, which is what a real conversation needs: knowing when someone has finished and when they have cut in, from the same model.

Its end-of-turn false-positive rate is **0.071**, so it wrongly claims the floor on seven of every hundred mid-turn pauses. That is the number users feel, because each one is the agent cutting somebody off mid-sentence.

Filtered by conversation type, it ranks **3rd of 18** on end-of-turn in casual, collaborative and narrative conversation.

<div align="center">

<a href="https://turnbench.sesame.com/models/echo-omni"><img src="https://img.shields.io/badge/%F0%9F%8F%86%20See%20the%20full%20scorecard-TurnBench%20Leaderboard-1D4ED8?style=for-the-badge&labelColor=0F2143" alt="See Echo Omni's full TurnBench scorecard"></a>

<sub>Scored by Sesame AI Labs. Ranks as of 11 September 2026.</sub>

</div>

---

## 🧠 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` | Semantic | **5 to 10 ms** | The lowest latency. The default when responsiveness matters most |
| `echo-large` | Semantic | **10 to 20 ms** | Higher accuracy, when it matters more than raw speed |
| **`echo-omni`** | **Audio + semantic** | **60 to 80 ms** | The widest language coverage, with acoustic understanding on top of the semantics |

---

## 🚀 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.

```python
# 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")
```

<div align="center">

<a href="https://zeroruntime.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface"><img src="https://img.shields.io/badge/Get%20access%20and%20start%20building-zeroruntime.ai-6C4CF1?style=for-the-badge" alt="Get Access"></a>
&nbsp;
<a href="https://docs.zeroruntime.ai/build/turn-detection-and-interruptions/turn-detection?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface#turn-detection"><img src="https://img.shields.io/badge/Turn%20detection%20%26%20interruptions-docs.zeroruntime.ai-1F2937?style=for-the-badge" alt="Documentation"></a>

</div>

---

<div align="center">

![Echo Omni](wordmark.svg)

<sub><b>Echo Omni</b> by <a href="https://zeroruntime.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface">Zero Runtime</a></sub>

</div>