Datasets:

phot0nqqq
/

AISHELL8-FISHEYE

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	./baseline/mixture/mixture_train/4033_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/4092_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/8273_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/11252_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/12952_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/138_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/164_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/199_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/1041_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/2131_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/2188_tar	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/2190_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/3737_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
	./baseline/mixture/mixture_train/3796_mix	hf://datasets/phot0nqqq/AISHELL8-FISHEYE@12f56bf930bc32cb7ff8fb926440f6212550de45/baseline.tar.gz
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End of preview. Expand in Data Studio

AISHELL8-FISHEYE: A Fisheye Audio-Visual Dataset for Target Speaker Extraction with Distortion-Aware Baselines

AISHELL8-FISHEYE is the first fisheye audio-visual dataset for target speaker extraction (TSE) in wide-angle scenarios, featuring synchronized audio and severely distorted video captured by fisheye cameras.

🎯 Overview

Audio-visual target speaker extraction remains challenging in visually distorted conditions such as those captured by fisheye cameras. This dataset addresses the lack of dedicated resources for fisheye audio-visual speech processing by providing:

Synchronized fisheye and normal camera videos with corresponding audio recordings
94 native Chinese speakers delivering spontaneous monologues
Significant radial distortion for realistic wide-angle scenarios
Paired undistorted videos for controlled comparison studies

📊 Dataset Statistics

Set	# Speakers	# Utterances	Duration
Training	66	10,308	32.75h
Validation	14	2,213	7.22h
Testing	14	2,270	7.06h
Total	94	14,791	47.03h

🏗️ Dataset Structure

The dataset follows the following directory structure:

└── AISHELL8_FISHEYE
    ├── 0065
    │   ├── seg_audio
    │   │   ├── utterance_1.txt
    │   │   ├── utterance_1.wav
    │   │   └── ... 
    │   └── seg_video
    │       ├── clip_1.mp4
    │       ├── clip_1.txt
    │       └── ... 
    ├── 0067
    │   ├── seg_audio
    │   │   ├── utterance_1.txt
    │   │   ├── utterance_1.wav
    │   │   └── ... 
    │   └── seg_video
    │       ├── clip_1.mp4
    │       ├── clip_1.txt
    │       └── ... 
    ├── ...

Data Specifications:

Video: 4K resolution (3840×2160) at 25 FPS (fisheye) + 720p (1280×720) at 25 FPS (normal)
Audio: 16 kHz sampling rate, mono channel
Transcripts: Natural Chinese monologues
SNR Range: Mixtures generated with SNR uniformly sampled from [-10, 10] dB

🎥 Demo

Visit our demo page: https://e181d4.github.io/AISHELL8-FISHEYE/

🧠 Baseline Model

We propose a distortion-aware baseline model for target speaker extraction from fisheye video:

Architecture

The model consists of three main components:

Distortion-Aware Encoder: Pre-trained branch for extracting distortion-invariant features from fisheye lip ROI
Lip Feature Extractor: ResNet-based encoder for capturing lip movement cues
Fusion and Separation Module: Combines visual features with audio for target speaker extraction

Key Features:

Coordinate Attention Mechanism: Captures fine-grained distortion patterns across the image plane
Inverted Residual Blocks: Efficient feature extraction with MobileNetV2 architecture
End-to-End Training: Joint optimization of distortion adaptation and speaker extraction

Performance

Our baseline achieves competitive performance on fisheye data:

Configuration	SI-SDRi (dB)	STOI	PESQ
Fisheye + DA Branch (ResNet Unfrozen)	11.84	0.81	2.96
Fisheye w/o DA Branch (ResNet Unfrozen)	10.47	0.78	2.93
Normal Video (Baseline)	19.33	0.90	3.41

📝 License & Citation

License

This dataset is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

You are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
NonCommercial — You may not use the material for commercial purposes.

See the LICENSE file for details.

Downloads last month: 32

Number of rows:

40,500

Estimated number of rows:

644,164

Total file size:

132 GB