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We introduce the Audio Logical Reasoning (ALR) dataset, consisting of 6,446 text-audio annotated samples specifically designed for complex reasoning tasks. Building on this resource, we propose SoundMind, a rule-based reinforcement learning (RL) algorithm tailored to endow audio language models (ALMs) with deep bimodal reasoning abilities.
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# SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models [EMNLP 2025 Main Conference (Oral)] [](https://github.com/xid32/SoundMind/blob/main/LICENSE) [](https://arxiv.org/abs/2506.12935) [](https://huggingface.co/datasets/SoundMind-RL/SoundMindDataset) [](https://www.dropbox.com/scl/fi/irtbrnmk5e0ecvv8fyrum/audio_dataset.zip?rlkey=p1ebkt9h1bkyjsq3fo2bp667v&st=gxr542e2&dl=0) This repository is the official implementation of *SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models* (EMNLP 2025). We introduce **SoundMind-RL**, a novel rule-based reinforcement learning framework that empowers large-scale audio-language models with advanced logical reasoning capabilities across both audio and textual modalities. To enable such training, we build the **SoundMind dataset**, an Audio Logical Reasoning (ALR) benchmark comprising 6,446 high-quality samples annotated with chain-of-thought reasoning in both audio and text forms. <p align="center"> <img src="./figs/f1.png" alt="Task Figure"> </p> ## Dataset Download To download our dataset, please visit this link: [Hugging Face](https://huggingface.co/datasets/SoundMind-RL/SoundMindDataset). Alternatively, you can also download it from [Dropbox](https://www.dropbox.com/scl/fi/irtbrnmk5e0ecvv8fyrum/audio_dataset.zip?rlkey=p1ebkt9h1bkyjsq3fo2bp667v&st=gxr542e2&dl=0). Run the following command: ```bash wget -c "https://www.dropbox.com/scl/fi/irtbrnmk5e0ecvv8fyrum/audio_dataset.zip?rlkey=p1ebkt9h1bkyjsq3fo2bp667v&st=gxr542e2&dl=1" -O audio_dataset.zip ``` The dataset contains train, test, and validation splits with corresponding text descriptions and metadata stored as JSON files. All annotation files are located in the `dataset-annotation-json` folder in this GitHub. ## Requirements ### Recommended Hardware 8× NVIDIA H800 80GB or 8× NVIDIA H100 80GB GPUs. ### Codebase and Compatibility Our codebase is based on [verl](https://github.com/volcengine/verl). If you are already familiar with [verl](https://github.com/volcengine/verl), you should be able to quickly get started with this repository. ### Environment Setup (Recommended: Anaconda) - **Python**: Version >= 3.9 - **CUDA**: Version >= 12.1 For training and inference engines to utilize better and faster hardware support, CUDA/cuDNN and other dependencies are required, and some of the dependencies are easy to be overridden when installing other packages. We need to install the following prerequisites: - **CUDA**: Version >= 12.4 - **cuDNN**: Version >= 9.8.0 ```bash # change directory to anywhere you like, in verl source code directory is not recommended wget https://developer.download.nvidia.com/compute/cudnn/9.8.0/local_installers/cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb dpkg -i cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb cp /var/cudnn-local-repo-ubuntu2204-9.8.0/cudnn-*-keyring.gpg /usr/share/keyrings/ apt-get update apt-get -y install cudnn-cuda-12 ``` Create and activate a new conda environment: ```bash conda create -n alr python==3.10 conda activate alr ``` Install verl: ```bash bash scripts/install_vllm_sglang_mcore.sh pip install --no-deps -e . ``` Please make sure that the installed packages are not overridden during the installation of other packages. The packages worth checking are: - **torch** and torch series - **vLLM** - **SGLang** - **pyarrow** - **tensordict** For [Qwen2.5-Omni](https://github.com/QwenLM/Qwen2.5-Omni), we need to update some additional library versions. ```bash pip install transformers==4.52.3 pip install accelerate pip install qwen-omni-utils[decord] -U ``` ## Preprocessing Data Our project and code rely on Audio Logical Reasoning
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matched fp:99536faeea0db773, topic:dataset, desc:dataset, readme:dataset
matched fp:99536faeea0db773, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning