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pretty_name: Helixbench
configs:
- config_name: default
data_files:
- split: test
path: data/test.zip
---
# 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](https://arxiv.org/abs/2609.38123) for the benchmark motivation and results.
## What's included
| File | Contents |
|---|---|
| [`data/test.zip`](https://huggingface.co/datasets/NoizAI/Helixbench/tree/main/data) | 1,015 first-frame images and their metadata |
| [`metadata.jsonl`](https://huggingface.co/datasets/NoizAI/Helixbench/blob/main/metadata.jsonl) | Sample IDs and AV captions in a directly readable file |
| [`evaluation.zip`](https://huggingface.co/datasets/NoizAI/Helixbench/blob/main/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:
```bash
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"
"$R/env/bin/helixbench" verify-package
```
Prepare a `generated.jsonl` containing exactly one MP4 per sample ID. Each row needs `sample_id`, an absolute `path`, and the file's `sha256`. See the [input format](evaluation/examples/INPUT_FORMAT.md) for the media requirements.
```bash
"$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](evaluation/provenance/DEPENDENCIES.md) 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](https://arxiv.org/abs/2609.38123).
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