---
license: apache-2.0
language:
- en
---
# 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.