Gatsby1984/MAC_SLU

MAC-SLU: A Benchmark for Multi-Intent Spoken Language Understanding in Automotive Cabins

Paper | Code

This repository hosts the MAC-SLU dataset, a novel Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark. MAC-SLU is designed to evaluate Spoken Language Understanding (SLU) systems on complex, multi-intent user commands within an automotive environment, addressing the limitations of existing SLU datasets in terms of diversity and complexity. It features authentic and complex multi-intent data, suitable for benchmarking both Large Language Models (LLMs) and Large Audio Language Models (LALMs).

🚀 Getting Started

1. Download the Dataset

The complete MAC-SLU dataset is hosted on the Hugging Face Hub.

• Dataset Link: Gatsby1984/MAC_SLU

2. Prepare the Environment

Our experiments are divided into two main approaches: In-Context Learning (ICL) and Supervised Fine-Tuning (SFT). Please set up the appropriate environment for the method you wish to use.

🛠️ Usage

In-Context Learning (ICL)

Environment Setup

Our ICL code relies on vLLM. The required version depends on the model you are using. All experiments were conducted with Python 3.10.

• For Qwen3 experiments: pip install vllm==0.9.2
• For Qwen2.5-Omni experiments: pip install vllm==0.8.5.post1

Running ICL Experiments

Step 1: Deploy the Model with vLLM (This step is not required if you are using a commercial API.)

Open a terminal and run the following command to start the vLLM server. This example is for Qwen2.5-Omni-7B.

export CUDA_VISIBLE_DEVICES=0

vllm serve /path/to/your/Qwen2.5-Omni-7B \
  --served-model-name Qwen2.5-Omni-7B \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.9 \
  --host 0.0.0.0 \
  --port 12355 \
  --uvicorn-log-level warning \
  --disable-log-requests \
  --max-model-len 32768

Step 2: Run Inference

Once the server is running, open a new terminal and execute the inference script.

python slu_icl.py \
    --provider local \
    --input-file /path/to/test_set.jsonl \
    --audio-dir /path/to/audio_test_directory \
    --output-file /path/to/prediction.jsonl \
    --model-name Qwen2.5-Omni-7B \
    --api-base http://0.0.0.0:12355/v1

• Note: For other models, you may need to change --model-name and the model path in the vllm serve command. To use a commercial API, change --provider to the appropriate name and configure the necessary API keys.

Step 3: Evaluation

python metrics.py prediction.jsonl icl_label.jsonl

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Supervised Fine-Tuning (SFT)

Environment Setup

For SFT experiments, we use the efficient LLaMA-Factory framework. Please follow the official instructions to install and set up the environment.

• Framework: LLaMA-Factory

Training Instructions

We recommend using a LoRA-SFT approach for fine-tuning.

1. Prepare your dataset using the format required by LLaMA-Factory.
2. Configure your training run by selecting a model, dataset, and setting the LoRA hyperparameters.