| --- |
| 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). |
|
|
