Duplicate from qualialabsAI/DuplexConv

Co-authored-by: Alex <Alexqualialabs@users.noreply.huggingface.co>

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README.md

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+---
+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)

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