BingoG/LTX

SpatialAV2AV — Spatial Audio-Video Editing Dataset

Source→edit pairs for training spatial (binaural/stereo) audio-video editing with LTX-2. Each pair renders the same clip under a different camera trajectory; the model learns source (pre-edit space) + trajectory instruction → edit (target space) for both video and the stereo sound field.

Contents

Each sample is a triple sharing one basename <video_id>+<traj>:

Path	What
final_json/<clip>.json	metadata: video_id, traj_name, width, height, fps, and relative paths to source/edit
final_edit/<clip>.mp4	target video — carries embedded 2-channel / 44.1 kHz stereo audio
final_source/<clip>.mp4	condition video (same clip, source camera) — also embedded stereo audio

Audio is inside the MP4s (AAC, real stereo). No separate .wav files are needed — the trainer reads the audio track directly from each MP4.

all.list lists the 116,147-pair training split (relative json paths). The final_source/ final_edit folders may contain a larger pool; all.list is the authoritative training set.

Camera trajectories: push_in, pull_out, pan_left, pan_right, rotate_left, rotate_right, fixed_left, fixed_right, fixed_rot_left, fixed_rot_right.

Sizes

• Training split: 116,147 pairs, ~147 GB (edit ~0.79 MB + source ~0.50 MB per pair).
• Resolutions: short side ~480; assorted aspect ratios. fps = 25.

Usage

from huggingface_hub import snapshot_download
root = snapshot_download("BingoG/LTX", repo_type="dataset")
# point the trainer's data.json_list at f"{root}/all.list"
# json paths inside are relative to `root`, so they resolve after download.

The training loader normalizes each final_json entry to (edit=target video+audio, source=condition video+audio) and derives the caption from traj_name.