---
language: zh
license: cc-by-nc-4.0
tags:
- speech
- conversational-speech
- chinese
pretty_name: DuplexConv
---

<p align="center">
  <img src="https://raw.githubusercontent.com/qualialabsAI/SmoothConv-DuplexConv/main/figs/logo_1.png" alt="SmoothConv & DuplexConv" width="640">
</p>

# DuplexConv

**DuplexConv** is a large-scale Chinese multi-channel conversational speech dataset with **LLM-assisted annotations**, developed by [ASLP@NPU](https://www.npu-aslp.org) and QualiaLabs as part of the SmoothConv–DuplexConv corpus family.

<p align="center">
  <a href="https://qualialabsai.github.io/SmoothConv-DuplexConv"><img src="https://img.shields.io/badge/Demo-Page-2563eb" alt="Demo Page"></a>
  <a href="https://huggingface.co/datasets/qualialabsAI/SmoothConv"><img src="https://img.shields.io/badge/SmoothConv-Companion%20Dataset-2563eb" alt="SmoothConv"></a>
  <a href="https://github.com/qualialabsAI/SmoothConv-DuplexConv"><img src="https://img.shields.io/badge/GitHub-Repo-green" alt="GitHub"></a>
</p>

**Companion dataset:** [**SmoothConv**](https://huggingface.co/datasets/qualialabsAI/SmoothConv) on HuggingFace (100 hours, expert human annotation). DuplexConv and SmoothConv share the same conversational domains and a unified data design. SmoothConv focuses on high-quality human annotations for benchmarking and supervised learning; DuplexConv emphasizes scale for Speech LLM pre-training and data-driven modeling.

## Dataset Overview

DuplexConv comprises **2,000 hours** of naturally occurring **multi-party Chinese conversations** recorded in **multi-channel** environments across **Tutoring** and **Social Chat** scenarios. The dataset captures realistic full-duplex conversational behaviors, including overlapping speech, backchannels, interruptions, pauses, and dynamic turn transitions.

An **LLM-assisted annotation pipeline** generates transcripts, speaker-aware conversational structures, turn-level interaction information, and scene-level contextual labels. Together with [SmoothConv](https://huggingface.co/datasets/qualialabsAI/SmoothConv), DuplexConv bridges fine-grained human annotation and large-scale Speech LLM training in realistic full-duplex settings.

| Metric | Value |
| :--- | :---: |
| **Total Duration** | 2,000.21 hours |
| **Audio Files** | 93,709 |
| **Mean Duration** | 76.84 sec |
| **Duration Range** | 8.0 – 618.3 sec |
| **Language** | Chinese (zh) |
| **Domains** | Tutoring, Social Chat |
| **Annotation** | LLM-assisted |

## Domains & Directory Layout

After download, each conversation is stored under a **top-level folder** whose name indicates the scenario. Match the folder prefix to the domain:

| Scenario | Folder prefix | Example |
| :--- | :--- | :--- |
| **Tutoring** | starts with `edu` or `Edu` | `Edu_20240101_001/` |
| **Social Chat** | starts with `none_Edu` | `none_Edu_20240101_001/` |

Within each folder you will find paired multi-channel audio (`.wav`) and annotation (`.json`) files. The same naming convention applies to both DuplexConv and SmoothConv.

## Dataset Statistics

<p align="center">
  <img src="https://raw.githubusercontent.com/qualialabsAI/SmoothConv-DuplexConv/main/figs/statics/DuplexConv.png" alt="DuplexConv statistics" width="720">
</p>

## Supported Tasks

- Speech Language Model (Speech LLM) pre-training
- Conversational speech understanding
- Turn-taking and interaction modeling
- Full-duplex spoken dialogue systems
- Multi-party conversational AI

## Annotation Format

Each audio file is paired with a JSON annotation file. The root object **`wavInfo`** has the following structure:

```
wavInfo
├── nTrack, timeLenInSec, fs          # channels, duration (s), sample rate (Hz)
├── vadFrmLenInMs                     # VAD frame length (ms)
├── vadFlagPerFrmPerTrack             # ndarray (nFrm, nTrack), frame-level VAD per channel
└── asr[track][sentence]              # per-channel, per-utterance ASR & labels
```

**`wavInfo` — audio metadata**

| Field | Type | Description |
| :--- | :--- | :--- |
| `nTrack` | `int` | Number of audio channels |
| `timeLenInSec` | `float` | Duration (seconds) |
| `fs` | `int` | Sample rate (Hz) |
| `vadFrmLenInMs` | `int` | VAD frame length (milliseconds) |
| `vadFlagPerFrmPerTrack` | `ndarray (nFrm, nTrack)` | Per-frame VAD flag per channel |
| `asr` | `list[nTrack]` | ASR results; `asr[i]` is a list of utterances on channel `i` |

**`asr[track][sentence]` — per-utterance fields**

| Field | Type | Description |
| :--- | :--- | :--- |
| `txt` | `str` | Transcript (primary ASR engine) |
| `startInMs` / `endInMs` | `int` | Start / end time (milliseconds) |
| `LID` | `str` | Language ID (e.g. `cn`) |
| `privacyFlag` | `bool` | `True` if sensitive content detected |
| `asrRes` | `dict` | Results from multiple open-source ASR engines |
| `labels` | `dict` | LLM labels: `gender`, `age`, `emotion`, `accent`, `paralinguistic`, `txt`, … |
| `state` | `str` | Turn state, e.g. `<\|complete\|>`, `<\|incomplete\|>` |
| `speaker` | `dict` | Speaker stats **within this utterance** (not global speaker IDs): `numSpeakers`, `multiSpeaker`, `segments` |
| `snr` / `mos` | `float` | Signal-to-noise ratio / MOS estimate |
| `AED_dasheng` | `list` | Audio event detection scores |

### Example utterance

```json
{
  "startInMs": 3664,
  "endInMs": 17280,
  "LID": "cn",
  "txt": "right away不是状语它就是副词嘛...",
  "privacyFlag": false,
  "state": "<|complete|>",
  "labels": {"gender": "男声", "age": "青年", "emotion": "...", "txt": "..."},
  "speaker": {"numSpeakers": 1, "segments": [{"speaker": "SPEAKER_00", "startSec": 3.664, "endSec": 10.264}]}
}
```

## Usage

```python
import json
import numpy as np
from datasets import load_dataset

ds = load_dataset("qualialabsAI/DuplexConv")

# Load annotation for a sample
with open("path/to/annotation.json", "r", encoding="utf-8") as f:
    wav_info = json.load(f)

print(wav_info["nTrack"], wav_info["timeLenInSec"], wav_info["fs"])

# Per-track sentences
for track_idx, track_asr in enumerate(wav_info["asr"]):
    for sent in track_asr:
        print(track_idx, sent["startInMs"], sent["endInMs"], sent.get("txt", sent.get("labels", {}).get("txt")))
```

## Ethics Statement

- **Informed consent.** Conversations were recorded with the knowledge and consent of participants. Personal identifiers have been removed or anonymized prior to release.
- **Privacy protection.** For academic and research use only. Do not attempt to re-identify speakers or reconstruct private information.
- **Intended use.** Research on spoken dialogue, turn-taking, and speech understanding—not for unauthorized surveillance, impersonation, or deceptive content generation.
- **Limitations & bias.** Labels are machine-assisted and may contain errors; account for domain, demographic, and annotation bias in experiments.
- **Responsible use.** Report suspected misuse to [jimz@qualialabs.ai](mailto:jimz@qualialabs.ai).

## License

[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)

## Citation

```bibtex
@article{wang2026duoconv,
  title   = {DuoConv: Large-Scale Chinese Full-Duplex Speech Datasets for Conversational AI},
  author  = {Chengyou Wang and Chunjiang He and Zhou Zhu and Lei Xie},
  journal = {arXiv preprint arXiv:0000.00000},
  year    = {2026},
  note    = {Placeholder; paper forthcoming}
}
```

## Contact

[jimz@qualialabs.ai](mailto:jimz@qualialabs.ai)