Instructions to use iiiiii123/AVBench_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iiiiii123/AVBench_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iiiiii123/AVBench_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
language:
- en
- zh
library_name: transformers
tags:
- avbench
- audio-text
- video-text
- audio-video
base_model:
- Qwen/Qwen2-Audio-7B-Instruct
- Qwen/Qwen2.5-Omni-7B
---
# AVBench Models
This repository hosts the evaluator models used in **AVBench**, a benchmark for text-to-audio-video generation quality and cross-modal consistency.
[](https://huggingface.co/datasets/iiiiii123/AVBench)
[](https://huggingface.co/iiiiii123/AVBench_model)
## AVBench in brief
AVBench evaluates generated content on two splits:
- **Normal split**: common, easier samples.
- **Hard split**: challenging samples with stronger cross-modal requirements.
It covers cross-modal alignment (Audio-Text / Video-Text / Audio-Video) and generation quality dimensions.
Dataset link:
- https://huggingface.co/datasets/iiiiii123/AVBench
## Model zoo used by AVBench
| Model | Use in AVBench | Trained / merged from |
|---|---|---|
| `Qwen2-Audio-7B-AudioTextMatching-Merged` | Audio-Text consistency scoring (AT) | `Qwen/Qwen2-Audio-7B-Instruct` |
| `Qwen2.5-Omni-7B-VideoTextMatching-Merged` | Video-Text consistency scoring (VT) | `Qwen/Qwen2.5-Omni-7B` |
| `Qwen2.5-Omni-7B-AudioVideoMatching-Merged` | Audio-Video consistency scoring (AV) | `Qwen/Qwen2.5-Omni-7B` |
## Notes
These models are released for AVBench evaluation and analysis.
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