Update README.md

README.md

@@ -3,4 +3,250 @@ license: apache-2.0
 language:
 - en
 ---
-这是SA-UAED中仿真并使用的Libripara数据集
+# LibriPara
+
+## Overview
+
+**LibriPara** is an audio dataset designed for research on **speaker-aware paralinguistic event detection** and **unified audio event detection**.
+
+It is associated with the paper:
+
+> **SA-UAED: Joint Frame-Level Detection of Audio Events, Speaker Activities, and Speaker-Attributed Paralinguistic Events**  
+>  Interspeech 2026.
+
+The dataset is intended to support joint modeling of:
+
+- audio events,
+- speaker activities,
+- speaker-attributed paralinguistic events,
+- frame-level temporal localization.
+
+Each sample is accompanied by the corresponding annotations and conversation-level metadata.
+
+---
+
+## Dataset Scale
+
+| Split      |      Duration |
+| ---------- | ------------: |
+| Train      | **500 hours** |
+| Validation |   **5 hours** |
+| Test       |   **5 hours** |
+| **Total**  | **510 hours** |
+
+---
+
+## Dataset Structure
+
+The repository is organized approximately as follows:
+
+```text
+Libripara/
+├── train/
+│   ├── audio/
+│   │   ├── 00000/
+│   │   ├── 00001/
+│   │   ├── ...
+│   │   └── 000xx/
+│   ├── labels/
+│   │   ├── 00000/
+│   │   ├── 00001/
+│   │   ├── ...
+│   │   └── 000xx/
+│   └── conversations/
+│       ├── 00000/
+│       ├── 00001/
+│       ├── ...
+│       └── 000xx/
+├── val/
+│   ├── audio/
+│   ├── labels/
+│   └── conversations/
+└── test/
+    ├── audio/
+    ├── labels/
+    └── conversations/
+```
+
+To avoid storing too many files in a single directory, large subsets are divided into multiple shard directories such as:
+
+```text
+00000/
+00001/
+00002/
+...
+```
+
+Files belonging to the same sample share the same file stem and are stored in the corresponding shard.
+
+For example:
+
+```text
+train/audio/00003/example_001.wav
+train/labels/00003/example_001.*
+train/conversations/00003/example_001.*
+```
+
+These files correspond to the same sample.
+
+---
+
+## Tasks
+
+LibriPara can be used for research on:
+
+- Unified Audio Event Detection
+- Sound Event Detection
+- Speaker Activity Detection
+- Speaker Diarization
+- Speaker-Aware Paralinguistic Event Detection
+- Non-Verbal Vocalization Detection
+- Laughter and Cough Detection
+- Frame-Level Audio Event Localization
+- Multi-Task Speech and Audio Understanding
+
+---
+
+## Download
+
+### Hugging Face CLI
+
+Install the Hugging Face Hub client:
+
+```bash
+pip install -U huggingface_hub
+```
+
+Then download the full dataset:
+
+```bash
+hf download originalover/Libripara \
+    --repo-type dataset \
+    --local-dir ./Libripara
+```
+
+The dataset will be saved to:
+
+```text
+./Libripara
+```
+
+### Python
+
+You can also download the dataset using `huggingface_hub`:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="originalover/Libripara",
+    repo_type="dataset",
+    local_dir="./Libripara",
+)
+```
+
+---
+
+## Reading the Dataset
+
+Because the training data are stored in multiple shard directories, recursive file traversal is recommended.
+
+```python
+from pathlib import Path
+
+root = Path("./Libripara")
+audio_root = root / "train" / "audio"
+
+audio_files = sorted(audio_root.rglob("*.wav"))
+
+print(f"Number of training audio files: {len(audio_files)}")
+
+for wav_path in audio_files[:5]:
+    print(wav_path)
+```
+
+To locate the corresponding label and conversation metadata:
+
+```python
+from pathlib import Path
+
+root = Path("./Libripara")
+
+audio_root = root / "train" / "audio"
+label_root = root / "train" / "labels"
+conversation_root = root / "train" / "conversations"
+
+wav_path = next(audio_root.rglob("*.wav"))
+
+relative_path = wav_path.relative_to(audio_root)
+shard = relative_path.parent
+sample_id = wav_path.stem
+
+label_candidates = list(
+    (label_root / shard).glob(f"{sample_id}.*")
+)
+
+conversation_candidates = list(
+    (conversation_root / shard).glob(f"{sample_id}.*")
+)
+
+print("Audio:", wav_path)
+print("Label:", label_candidates)
+print("Conversation:", conversation_candidates)
+```
+
+The exact file extensions of labels and metadata depend on the released dataset version.
+
+---
+
+## Recommended Usage
+
+When implementing a custom PyTorch `Dataset`, recursively search the audio directory instead of assuming that all audio files are directly stored under `train/audio/`.
+
+```python
+from pathlib import Path
+
+self.audio_files = sorted(
+    Path("Libripara/train/audio").rglob("*.wav")
+)
+```
+
+When locating the corresponding label and conversation metadata, preserve the same shard path.
+
+---
+
+## Citation
+
+If you use **LibriPara** in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{lan26_interspeech,
+  title     = {{SA-UAED: Joint Frame-Level Detection of Audio Events, Speaker Activities, and Speaker-Attributed Paralinguistic Events}},
+  author    = {Zekun Lan and Wangyou Zhang and Yanmin Qian},
+  year      = {2026},
+  booktitle = {{Interspeech 2026}},
+  pages     = {1406--1410},
+  doi       = {10.21437/Interspeech.2026-2486},
+  issn      = {2958-1796},
+}
+```
+
+---
+
+## License
+
+Please refer to the licenses and terms of use of the original source datasets and resources used to construct LibriPara.
+
+Users are responsible for ensuring that their use of this dataset complies with the corresponding licenses and terms.
+
+---
+
+## Repository
+
+Hugging Face:
+
+```text
+https://huggingface.co/datasets/originalover/Libripara
+```
+
+For questions regarding the dataset, annotations, or benchmark settings, please open an issue in the Hugging Face dataset repository.