StresSLM

--- library_name: transformers language: - en base_model: - Qwen/Qwen2-Audio-7B-Instruct pipeline_tag: audio-text-to-text tags: - lora license: cc-by-nc-4.0 --- # StresSLM **StresSLM** is an audio-text-to-text model fine-tuned with LoRA adapters on top of the [`Qwen/Qwen2-Audio-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) base model. It is designed to tackle **Sentence Stress Detection (SSD)** and **Sentence Stress Reasoning (SSR)** tasks on the StressTest benchmark. StresSLM predicts **stress patterns** and **reasoning** based on spoken audio. For more information, see our paper and code: 💻 [Code](https://github.com/slp-rl/StressTest) | 🤗 [StressTest Dataset](https://huggingface.co/datasets/slprl/StressTest) | 🤗 [Stress-17k Dataset](https://huggingface.co/datasets/slprl/Stress-17K-raw) 📃 [StressTest Paper](https://arxiv.org/abs/2505.22765) | 🌐 [Project Page](https://pages.cs.huji.ac.il/adiyoss-lab/stresstest/) --- ## Usage This model can be loaded using the HuggingFace Transformers library: ```python from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration from peft import PeftModel, PeftConfig # Load processor processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct") # Load LoRA config and base model peft_config = PeftConfig.from_pretrained("slprl/StresSLM") base_model = Qwen2AudioForConditionalGeneration.from_pretrained(peft_config.base_model_name_or_path) # Load LoRA adapter model = PeftModel.from_pretrained(base_model, "slprl/StresSLM") ``` --- ## Tasks * **Sentence Stress Detection (SSD)**: Identify stressed words in an utterance. * **Sentence Stress Reasoning (SSR)**: Reason about the speaker’s intention using stress patterns. For evaluation scripts and benchmarks, refer to the [StressTest GitHub repository](https://github.com/slp-rl/StressTest). --- ## 📖 Citation If you use this model, please cite: ```bibtex @misc{yosha2025stresstest, title={StressTest: Can YOUR Speech LM Handle the Stress?}, author={Iddo Yosha and Gallil Maimon and Yossi Adi}, year={2025}, eprint={2505.22765}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.22765}, } ```

Source context: 0 downloads · 3 likes · Pipeline audio-text-to-text · Library transformers · Repo slprl/StresSLM