README.md

---

tags:
  - audio
  - speech
  - chinese
  - mandarin
  - audiovisual
  - avsr
  - multimodal
  - far-field
  - multi-view
  - conversation

license: cc-by-nc-sa-4.0

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  Access will be granted only for academic and non-commercial research use.
  Please use your institutional email address and agree to the dataset license terms.

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---


## 📘 Dataset Summary

| **Property**         | **Description**             |
| :------------------- | :-------------------------- |
| **Language**         | Chinese (Mandarin, ZH)      |
| **License**          | CC BY-NC-SA 4.0             |
| **Total Duration**   | 102.19 hours                |
| **Speakers**         | 171 foreground speakers     |
| **Scenes**           | 5 real-world locations      |
| **Recording Style**  | Conversational speech       |
| **Audio**            | Near-field + far-field      |
| **Video**            | Multi-view RGB facial video |
| **Sampling Rate**    | 16 kHz                      |
| **Far-field Audio**  | 8-channel                   |
| **Video Resolution** | 256×256 @ 25 fps           |

---

## 🎙️ Dataset Description

AISHELL8-RealScene is a **public multi-scenario and multi-view audio-visual conversational corpus** recorded in real-world environments.

Unlike most existing AVSR datasets collected under constrained frontal settings, AISHELL8-RealScene focuses on realistic conditions including:

* environmental noise
* viewpoint variation
* speaker interference
* background activities
* outdoor scenes
* conversational interactions

The corpus was collected by AISHELL using a synchronized multi-device recording setup.

---

## 🏛️ Recording Environment

AISHELL8-RealScene contains recordings from **five real-world locations**:

| ID | Location | Type    |
| -- | -------- | ------- |
| L1 | Building | Outdoor |
| L2 | Hall     | Indoor  |
| L3 | Hotel    | Indoor  |
| L4 | Park     | Outdoor |
| L5 | Street   | Outdoor |

The recordings cover both indoor and outdoor scenarios.

---

## 🔊 Audio Specifications

AISHELL8-RealScene provides both:

* **near-field audio**
* **far-field microphone-array audio**

### Near-field Audio

Each foreground speaker wears a close-talking microphone used for transcription and alignment.

### Far-field Audio

Far-field audio is recorded using:

* circular microphone array
* 16 microphones
* 16 kHz
* 16-bit

To reduce release size while preserving spatial information, the public release provides:

* **8-channel far-field audio**

obtained by uniformly selecting microphones from the array.

### Audio Format

* Format: `.wav`
* Sampling rate: **16 kHz**
* Near-field: single-channel
* Far-field: 8-channel

---

## 🎥 Video Specifications

Video is captured using **three synchronized HD RGB cameras**.

### Camera Views

The three cameras are positioned horizontally with approximately **30° angular intervals**:

* **D0** — left
* **D1** — center
* **D2** — right

### Video Parameters

| Property   | Value        |
| ---------- | ------------ |
| Resolution | 256×256     |
| Frame Rate | 25 fps       |
| Views      | D0 / D1 / D2 |

The released facial videos are:

* **256×256** resolution
* synchronized with audio

---

## 📊 Dataset Statistics

### Dataset Split

| Split | Duration (h) | Sessions | Groups | Speakers |
| ----- | -----------: | -------: | -----: | -------: |
| Train |        79.84 |      133 |     56 |      133 |
| Dev   |        10.70 |       18 |      7 |       18 |
| Eval  |        11.65 |       20 |      7 |       20 |
| Total |       102.19 |      171 |     70 |      171 |

No speakers overlap across:

* train
* dev
* eval

All splits contain recordings from all five locations.

---



## 📜 License

This dataset is released under:

**CC BY-NC-SA 4.0**

https://creativecommons.org/licenses/by-nc-sa/4.0/

---

## 📄 Dataset Resources

- 📄 **Paper:** [arXiv:](https://arxiv.org/abs/)  
- 🌐 **Demo:** [xxx.github.io/AISHELL8-RealScene](https://xxx.github.io/AISHELL8-RealScene/)  


---

## 📜 Citation

If you use AISHELL8-RealScene, please cite:

```bibtex

@article{su2026m2savsr,

  title   = {M2S-AVSR: Modality-aware Multi-view Self-supervised Representation for Robust Audio-Visual Speech Recognition},

  author  = {Su, Fei and Li, Cancan and Li, Ming and Liu, Juan},

  journal = {},

  year    = {2026}

}

```