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README.md

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 ---
-license: apache-2.0
+language: zh
+license: cc-by-nc-4.0
+tags:
+- speech
+- conversational-speech
+- chinese
+pretty_name: SmoothConv
 ---
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/qualialabsAI/SmoothConv-DuplexConv/main/figs/logo_1.png" alt="SmoothConv & DuplexConv" width="640">
+</p>
+
+# SmoothConv
+
+**SmoothConv** is a high-precision Chinese multi-channel conversational speech dataset with **expert human annotations**, released as part of the [SmoothConv & DuplexConv](https://github.com/qualialabsAI/SmoothConv-DuplexConv) project by [ASLP@NPU](https://www.npu-aslp.org) and QualiaLabs.
+
+<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/DuplexConv"><img src="https://img.shields.io/badge/DuplexConv-Companion%20Dataset-059669" alt="DuplexConv"></a>
+  <a href="https://github.com/qualialabsAI/SmoothConv-DuplexConv"><img src="https://img.shields.io/badge/GitHub-Repo-green" alt="GitHub"></a>
+</p>
+
+## Dataset Summary
+
+SmoothConv provides **~100 hours** of real-world, unscripted **multi-party Chinese dialogue** recorded in **multi-channel** settings across **Tutoring** and **Social Chat** scenarios. Conversations capture natural full-duplex interaction phenomena—including overlapping speech, backchannels, interruptions, and pauses.
+
+Human annotators provide fine-grained labels suitable for benchmarking and supervised training on turn-taking, overlap detection, and spoken dialogue understanding.
+
+| Metric | Value |
+| :--- | :---: |
+| **Total Duration** | 100.53 hours |
+| **Audio Files** | 2,503 |
+| **Mean Duration** | 144.59 sec |
+| **Duration Range** | 60.0 – 634.7 sec |
+| **Language** | Chinese (zh) |
+| **Domains** | Tutoring, Social Chat |
+| **Annotation** | Expert human annotation |
+
+## Dataset Statistics
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/qualialabsAI/SmoothConv-DuplexConv/main/figs/statics/SmoothConv.png" alt="SmoothConv statistics" width="720">
+</p>
+
+Turn-taking labels include **complete**, **incomplete**, **backchannel**, and **wait**.
+
+## Supported Tasks
+
+- Automatic speech recognition (ASR) on spontaneous multi-party speech
+- Voice activity detection (VAD) and turn-taking modeling
+- Overlap / floor-holding detection
+- Turn-state prediction
+- Paralinguistic event detection (laughter, coughs, breaths, background noise, silence, etc.)
+- Speaker attribute modeling (gender, age, emotion)
+
+## Annotation Format
+
+Each audio file is paired with a JSON annotation file. The top-level object contains an **`instances`** list; each element describes one annotated segment.
+
+### Top-level structure
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `instances` | `list` | List of annotated segments in the conversation |
+
+### `instances[i]` — per-segment annotation
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `id` | `str` | Unique segment identifier (UUID) |
+| `channelIndex` | `int` | Audio channel index (0-based) |
+| `start` | `float` | Segment start time in **seconds** |
+| `end` | `float` | Segment end time in **seconds** |
+| `text` | `str` | Human-annotated transcript; inline tags mark events (e.g. `<pause>`, `<噪声>`, `<unclear>`) |
+| `attributes` | `dict` | Speaker, turn, paralinguistic, and scene attributes (see below) |
+
+### `instances[i].attributes`
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `speaker` | `str` | Speaker ID (e.g. `A1`, `B1`); `unknown` if unidentifiable |
+| `turn` | `str` | Turn-taking state: `complete`, `incomplete`, `backchannel`, `wait` |
+| `other_turn` | `list` (optional) | Co-occurring interaction cues, e.g. `pause`, `unknown turn` |
+| `gender` | `str` | Speaker gender |
+| `age` | `str` | Speaker age group (e.g. `adult`, `child`) |
+| `emotion` | `str` | Emotion label for the segment |
+| `speech event` | `list` | Paralinguistic / non-speech events (e.g. `nonespeech event`, `echo`, `shouting`) |
+| `这段数据是在什么环境` | `str` | Scene / environment description |
+
+### Example segment
+
+```json
+{
+  "id": "0d0687e7-b2e5-4b91-834b-f3e8988e7a4a",
+  "channelIndex": 0,
+  "start": 0.702,
+  "end": 5.146,
+  "attributes": {
+    "speaker": "A1",
+    "turn": "complete",
+    "gender": "male",
+    "age": "adult",
+    "emotion": "neutral",
+    "speech event": ["nonespeech event"],
+    "这段数据是在什么环境": "unknown"
+  },
+  "text": "春风花草香迟日江山丽日出江花红胜火"
+}
+```
+
+## Usage
+
+```python
+import json
+from datasets import load_dataset
+
+ds = load_dataset("qualialabsAI/SmoothConv")
+
+# Load annotation for a sample
+with open("path/to/annotation.json", "r", encoding="utf-8") as f:
+    anno = json.load(f)
+
+for seg in anno["instances"]:
+    print(seg["channelIndex"], seg["start"], seg["end"], seg["text"])
+```
+
+## Relation to DuplexConv
+
+SmoothConv and [DuplexConv](https://huggingface.co/datasets/qualialabsAI/DuplexConv) are complementary datasets drawn from the same or closely related conversation sources:
+
+- **SmoothConv** (~100h): expert human annotations for precision and benchmarking
+- **DuplexConv** (~2,000h): LLM-assisted annotations for large-scale coverage
+
+A turn-state evaluation subset derived from SmoothConv is also available as the [FastTurn Test Set](https://huggingface.co/datasets/ASLP-lab/FastTurn-Testset).
+
+## 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.** Human annotations may contain errors; account for domain and demographic 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 Jingbin Hu and Shuiyuan Wang and Bo Wu and Yuyu Ji and Jimeng Zheng and Ruofei Chen and Lei Xie},
+  journal = {arXiv preprint arXiv:0000.00000},
+  year    = {2026},
+  note    = {Placeholder; paper forthcoming}
+}
+```
+
+## Contact
+
+[jimz@qualialabs.ai](mailto:jimz@qualialabs.ai)