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diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dae9b4d47a4cd6393ec2546a779632b34e71098f --- /dev/null +++ b/README.md @@ -0,0 +1,168 @@ +--- +language: zh +license: cc-by-nc-4.0 +tags: +- speech +- conversational-speech +- chinese +pretty_name: DuplexConv +--- + +

+ SmoothConv & DuplexConv +

+ +# 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. + +

+ Demo Page + SmoothConv + GitHub +

+ +**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 + +

+ DuplexConv statistics +

+ +## 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) diff --git 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