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transformers — PCLM requires the custom
modeling code shipped in the release repo.1git clone https://github.com/ihp-lab/VoxParadox
2cd VoxParadox
3conda create -n qwen2audio python=3.10 -y && conda activate qwen2audio
4pip install torch torchaudio transformers accelerate librosa soundfile1python -m qwen2audio.eval.run_eval \
2 --model_path IHP-Lab/Qwen2-Audio_PCLM_DPO \
3 --data_path /path/to/voxparadox.json \
4 --audio_base /path/to/audio_root \
5 --output_dir runs/eval/qwen2audio_pclm_dpoeval.py:python eval.py --predictions runs/eval/qwen2audio_pclm_dpo/predictions.jsonluse_pclm=True from config.json and activates PCLM with
expose_layers=[5, 15, 25, 30] over the audio encoder.| Resource | Link |
|---|---|
| Paper (arXiv) | https://arxiv.org/abs/2605.27772 |
| Project page | https://voxparadox.github.io/ |
| Code | https://github.com/ihp-lab/VoxParadox |
| Benchmark | https://huggingface.co/datasets/IHP-Lab/VoxParadox |
| Sibling model (AF3) | https://huggingface.co/IHP-Lab/AF3_PCLM_DPO |
1@inproceedings{pang2026voxparadox,
2 title = {Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox},
3 author = {Pang, Jiacheng and Chaubey, Ashutosh and Soleymani, Mohammad},
4 booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
5 year = {2026}
6}LICENSE.Qwen/Qwen2-Audio-7B-Instruct) carries its own Tongyi Qianwen license terms,
which continue to apply to the inherited weights.