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HelixBench
A benchmark for audio-video generation and viewpoint-aligned spatial sound.
HelixBench contains 1,015 fixed test cases and evaluates generated videos with six metrics. Each case provides a first frame and one audio-video caption (caption_av). See the HelixWorld paper for the benchmark motivation and results.
What's included
| File | Contents |
|---|---|
data/test.zip |
1,015 first-frame images and their metadata |
metadata.jsonl |
Sample IDs and AV captions in a directly readable file |
evaluation.zip |
Evaluation code and frozen reference features |
The release contains AV captions only. Original and generated videos are not bundled; provide your model's generated MP4 for each sample ID.
Metrics
| Metric | Cases | Measures |
|---|---|---|
| KL ↓ | 1,015 | Audio distribution similarity using PaSST |
| FAD ↓ | 1,015 | Audio quality using VGGish features |
| DeSync (s) ↓ | 165 event cases | Audio-video synchronization |
| ImageBind ↑ | 1,015 | Video-audio alignment |
| CLAP ↑ | 1,015 | Generated audio against caption_av |
| Spatial ↑ | 150 perspective cases | Whether spatial sound follows viewpoint motion |
Spatial uses the v3 all-150 protocol: invalid clips score −100, eligible center-only clips score 0, and the remaining clips use signed lateral-frame correctness. The six metrics are reported separately; there is no combined score.
Quick start
On Linux with a compatible NVIDIA GPU, install python3, git, curl, unzip, ffmpeg, and ffprobe, then run:
mkdir -p helixbench && cd helixbench
curl -fL --retry 3 -o evaluation.zip \
https://huggingface.co/datasets/NoizAI/Helixbench/resolve/main/evaluation.zip
unzip -q evaluation.zip
R="$PWD/runtime"
bash evaluation/install.sh "$R"
"$R/env/bin/python" evaluation/download_weights.py "$R"
"$R/env/bin/helixbench" verify-package
Prepare a generated.jsonl containing exactly one MP4 per sample ID. Each row needs sample_id, an absolute path, and the file's sha256. See the input format for the media requirements.
"$R/env/bin/helixbench" prepare \
--generated /absolute/generated.jsonl \
--model-id YOUR_MODEL \
--run-dir /absolute/run
"$R/env/bin/helixbench" run \
--run-dir /absolute/run --metric all \
--deps-root "$R/deps" \
--weights-root "$R/weights" \
--torch-home "$R/torch"
"$R/env/bin/helixbench" aggregate --run-dir /absolute/run
"$R/env/bin/helixbench" verify --run-dir /absolute/run
The installer pins the third-party source revisions and checks downloaded weights. See dependencies and provenance for exact versions and hashes. The full workflow has been tested on one NVIDIA A10G.
Citation
If you use HelixBench, please cite HelixWorld: A Real-time Interactive Audio-Visual World Model.
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