btlam/VAVD

Vietnamese Visual-Audio Dataset

Vietnamese audio-visual landmark dataset designed for talking-face landmark generation. The dataset is curated to support efficient fine-tuning of the pretrained IP-LAP model on Vietnamese speakers.

Note: This dataset does not directly host video files. Instead, video_sources.csv provides the 123 public YouTube source URLs. The full dataset can be automatically downloaded and processed using download_dataset.py.

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Dataset Summary

Split	Clips	Speakers
Train	3,541	~118
Val	338	~66
Test	239	~52
Total	4,118	123

The dataset is constructed from two sources:

• vilap_dataset: 1,630 clips from the main training set
• vilap_dataset_extra_10h: 2,488 clips from the extended crawl

The videos are collected from public Vietnamese YouTube sources, including news programs, interviews, and talk shows. Each sample is a short single-speaker clip of approximately 5 seconds, processed at 25 FPS.

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Quick Start

# 1. Clone the dataset repository from Hugging Face
git clone https://huggingface.co/datasets/btlam2002/VAVD
cd VAVD

# 2. Install the required dependencies
pip install yt-dlp mediapipe opencv-python librosa scipy numpy

# 3. Download and process the full dataset
# This automatically downloads videos, cuts clips, extracts faces, landmarks, and audio features
python download_dataset.py --output ./vilap_data

# Or download only the test split for a quick trial (~239 clips)
python download_dataset.py --output ./vilap_data --splits test

# Or process only specific videos
python download_dataset.py --output ./vilap_data --video_ids 3p7dFrIx5bk xwsPD6xiPbI

The download_dataset.py pipeline automatically performs the following steps:

1. Download videos from YouTube using yt-dlp with a maximum resolution of 720p.
2. Cut clips into 5-second segments using ffmpeg.
3. Extract face crops at 128×128 pixels using MediaPipe Face Detection.
4. Extract landmarks using MediaPipe Face Mesh, including 74 pose landmarks and 57 content landmarks.
5. Extract audio features, including 16 kHz WAV files and Mel-spectrogram .npy files following the IP-LAP convention.

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Video Sources

All video URLs are listed in video_sources.csv.

Column	Description
video_id	YouTube video ID, also used as the speaker folder name
youtube_url	Full YouTube URL
title	Video title
channel	YouTube channel name
source	Dataset source, either vilap_dataset or vilap_dataset_extra_10h
n_train_clips	Number of clips in the training split
n_val_clips	Number of clips in the validation split
n_test_clips	Number of clips in the test split
n_total_clips	Total number of clips from the video

Example:

video_id,youtube_url,title,channel,source,n_train_clips,n_val_clips,n_test_clips,n_total_clips
3p7dFrIx5bk,https://www.youtube.com/watch?v=3p7dFrIx5bk,Chuyên gia phân tích...,VTV24,vilap_dataset,68,1,1,70

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Directory Structure

vilap_dataset/
├── 02_clips/
│   └── <speaker_id>/
│       └── <speaker_id>_cNNNN.mp4       # raw clip, 25 FPS, 1280×720
├── 03_face/
│   └── <speaker_id>/<clip_id>/
│       └── {frame_id}.png               # 128×128 face crop
├── 04_sketch/
│   └── <speaker_id>/<clip_id>/
│       └── {frame_id}.png               # optional sketch image
├── 05_landmark/
│   └── <speaker_id>/<clip_id>/
│       └── {frame_id}.npy               # per-frame landmark dictionary
└── 06_audio/
    └── <speaker_id>/<clip_id>/
        ├── audio.npy                    # Mel-spectrogram, shape [T, 80]
        └── audio.wav                    # 16 kHz mono WAV file

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Landmark Format

Each .npy landmark file contains a Python dictionary and can be loaded with allow_pickle=True.

d = np.load("05_landmark/spk/spk_c0000/0.npy", allow_pickle=True).item()
# d.keys() → ['pose_landmarks', 'content_landmarks']

Field	Points	Description
pose_landmarks	74	Jaw and upper-face structural landmarks
content_landmarks	57	Lip and lower-face landmarks, including jaw 0:17, outer lip 17:37, and inner lip 37:57

Each landmark is stored as a list of [landmark_id, x, y] triplets, where x and y are normalized to the face crop range [0.0, 1.0].

# Example: read one frame
import numpy as np

d = np.load("frame_0.npy", allow_pickle=True).item()

content = d["content_landmarks"]   # list of [id, x, y], 57 points
pose = d["pose_landmarks"]         # list of [id, x, y], 74 points

# Sort landmarks according to the IP-LAP ori_sequence_idx before feeding them into the model.

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Audio Format

Property	Value
Sample rate	16,000 Hz
Channels	Mono
Mel bands	80
FFT size	800
Hop size	200 samples
Frame rate	25 FPS, aligned with video

# Load Mel-spectrogram
mel = np.load("audio.npy")   # shape: [T_mel_frames, 80]

# Extract a 16-frame Mel window for frame i following the IP-LAP convention
frame_idx = 50
audio_offset = -2           # -4 recommended offset for Vietnamese speech

start = int(80.0 * ((frame_idx + audio_offset) / 25.0))
window = mel[start : start + 16, :]   # shape: [16, 80]

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File Lists

The filelists/ directory provides pre-computed train, validation, and test splits.

filelists/
├── train.txt   # 3,541 clips, 86.0%
├── val.txt     # 338 clips, 8.2%
└── test.txt    # 239 clips, 5.8%

Each line stores a clip key in the format <speaker_id>/<clip_id>.

3p7dFrIx5bk/3p7dFrIx5bk_c0000
3p7dFrIx5bk/3p7dFrIx5bk_c0001
...

The following example shows how to resolve the actual data paths:

from pathlib import Path

DATA_BASE = Path("path/to/vilap_dataset")

clip_key = "3p7dFrIx5bk/3p7dFrIx5bk_c0017"
dirname, vidname = clip_key.split("/")

face_dir = DATA_BASE / "03_face" / dirname / vidname
lm_dir = DATA_BASE / "05_landmark" / dirname / vidname
audio_npy = DATA_BASE / "06_audio" / dirname / vidname / "audio.npy"
audio_wav = DATA_BASE / "06_audio" / dirname / vidname / "audio.wav"

---

Loading a Full Clip

import numpy as np
import torch
from pathlib import Path

DATA_BASE = Path("vilap_dataset")

ORI_SEQUENCE_IDX = [
    162, 127, 234, 93, 132, 58, 172, 136, 150, 149, 176, 148, 152,
    377, 400, 378, 379, 365, 397, 288, 361, 323, 454, 356, 389,
    70, 63, 105, 66, 107, 55, 65, 52, 53, 46,
    336, 296, 334, 293, 300, 276, 283, 282, 295, 285,
    168, 6, 197, 195, 5,
    48, 115, 220, 45, 4, 275, 440, 344, 278,
    33, 246, 161, 160, 159, 158, 157, 173, 133, 155, 154, 153, 145, 144, 163, 7,
    362, 398, 384, 385, 386, 387, 388, 466, 263, 249, 390, 373, 374, 380, 381, 382,
    61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291, 375, 321, 405, 314, 17, 84, 181, 91, 146,
    78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95,
]


def load_clip(clip_key, data_base):
    dirname, vidname = clip_key.split("/")
    lm_dir = data_base / "05_landmark" / dirname / vidname

    npys = sorted(lm_dir.glob("*.npy"), key=lambda p: int(p.stem))
    frame_ids = [int(p.stem) for p in npys]

    pose_list, content_list = [], []

    for fid in frame_ids:
        d = np.load(lm_dir / f"{fid}.npy", allow_pickle=True).item()

        for key, pts, n in [
            ("pose_landmarks", pose_list, 74),
            ("content_landmarks", content_list, 57),
        ]:
            lm = sorted(d[key], key=lambda t: ORI_SEQUENCE_IDX.index(int(t[0])))

            arr = torch.zeros(2, n)
            arr[0] = torch.tensor([float(t[1]) for t in lm])
            arr[1] = torch.tensor([float(t[2]) for t in lm])

            pts.append(arr)

    all_pose = torch.stack(pose_list)        # shape: [N, 2, 74]
    all_content = torch.stack(content_list)  # shape: [N, 2, 57]

    mel = np.load(data_base / "06_audio" / dirname / vidname / "audio.npy")

    return all_pose, all_content, mel, frame_ids


pose, content, mel, fids = load_clip(
    "3p7dFrIx5bk/3p7dFrIx5bk_c0017",
    DATA_BASE,
)

print(
    f"Frames: {len(fids)}, "
    f"pose: {pose.shape}, "
    f"content: {content.shape}, "
    f"mel: {mel.shape}"
)

---

Processing New Videos

To generate the dataset from raw videos, use the pipeline provided in scripts/process_vilap_data_pipeline.py.

Step 1: Download or prepare raw .mp4 videos  →  02_clips/
Step 2: Detect and crop faces                →  03_face/
Step 3: Generate sketches, optional           →  04_sketch/
Step 4: Extract MediaPipe landmarks           →  05_landmark/
Step 5: Extract and resample audio            →  06_audio/

Requirements:

• Python 3.10.2
• mediapipe
• librosa
• opencv-python
• ffmpeg

For Mel-spectrogram extraction details consistent with IP-LAP, see scripts/preprocess_audio.py.

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License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License, CC BY-NC 4.0.

The original videos are sourced from publicly available YouTube content. The dataset is intended for research and non-commercial use only.