zhaoxiaoxian/SABER-Dataset

Integrating Fine-Grained Audio-Visual Evidence for Robust Multimodal Emotion Reasoning

This is the official implementation of the paper: "Integrating Fine-Grained Audio-Visual Evidence for Robust Multimodal Emotion Reasoning".

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💡 Key Contributions

• SABER Dataset: A large-scale multimodal emotion reasoning dataset containing ~600K video clips, featuring a unique six-dimensional annotation schema.
• SED Paradigm: Structured Evidence Decomposition forces the model to disentangle and analyze uni-modal evidence (Visual, Acoustic, etc.) before synthesizing a final emotional conclusion.
• CA-DPO: Consistency-Aware Direct Preference Optimization refines the model's judgment in modality-conflicting scenarios (e.g., a "sarcastic smile" with a "hostile tone").
• SOTA Performance: Outperforms existing open-source baselines on EMER, EmoBench-M, and SABER-Test.

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🔗 Resources

• Paper: https://arxiv.org/abs/2601.18321
• Model Weights (7B): https://huggingface.co/zhaoxiaoxian/SABER-LLM
• SABER Dataset: https://huggingface.co/datasets/zhaoxiaoxian/SABER-Dataset
• GitHub: https://github.com/zxzhao0/SABER-LLM

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📚 The SABER Dataset

SABER (Scene, Audio, Body, Expression, and Reasoning) is designed to shift multimodal emotion analysis from static classification to generative reasoning. It mitigates uni-modal dominance and hallucinations by grounding reasoning in observable multimodal evidence.

• Scale: ~600K video clips.
• Languages: Chinese (CN) and English (EN).
• Format: JSONL structure decoupled into explicit visual/acoustic evidence and holistic reasoning.

Six-Dimensional Annotation

1. Video Description: Macro scene context.
2. Facial Expression: Micro-expressions and gaze.
3. Body Language: Posture, gestures, and social signals.
4. Acoustic Features: Prosody, pitch, and tonal intensity.
5. Speech Content: Verbatim transcripts and semantic info.
6. Holistic Reasoning: Final causal logic and emotion analysis.

Click to view the JSON Data Structure

{
    "audio_desc": {
        "speech_content": "...",
        "acoustic_feat": "..."
    },
    "video_desc": "...",
    "facial_exp_desc": "...",
    "body_lang_desc": "...",
    "sentiment_analysis": "..."
}

Click to view Source Datasets & Related Work

SABER aggregates and builds upon several foundational multimodal datasets:

• CREMA-D: Houwei Cao, David G. Cooper, Michael K. Keutmann, Ruben C. Gur, Ani Nenkova, and Ragini Verma, "CREMA-D: crowd-sourced emotional multimodal actors dataset," IEEE Trans. Affect. Comput., vol. 5, no. 4, pp. 377-390, 2014.
• MEAD: Kaisiyuan Wang, Qianyi Wu, Linsen Song, Zhuoqian Yang, Wayne Wu, Chen Qian, Ran He, Yu Qiao, and Chen Change Loy, "Mead: a large-scale audio-visual dataset for emotional talking-face generation," in ECCV, 2020, pp. 700-717.
• MELD: Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, et al., "MELD: A multimodal multi-party dataset for emotion recognition in conversations," in ACL, 2019, pp. 527-536.
• MEIJU25: Rui Liu, Haolin Zuo, Zheng Lian, Xiaofen Xing, Björn W. Schuller, et al., "Emotion and intent joint understanding in multimodal conversation: A benchmarking dataset," CoRR, vol. abs/2407.02751, 2024.
• MER25: Zheng Lian, Rui Liu, Kele Xu, Bin Liu, Xuefei Liu, et al., "MER 2025: When affective computing meets large language models," CoRR, vol. abs/2504.19423, 2025.
• MSP-IMPROV: Carlos Busso, Srinivas Parthasarathy, Alec Burmania, Mohammed Abdel-Wahab, Najmeh Sadoughi, et al., "MSP-IMPROV: an acted corpus of dyadic interactions to study emotion perception," IEEE Trans. Affect. Comput., vol. 8, no. 1, pp. 67-80, 2017.
• MultiDialog: Se Jin Park, Chae Won Kim, Hyeongseop Rha, Minsu Kim, Joanna Hong, et al., "Let's go real talk: Spoken dialogue model for face-to-face conversation," in ACL, 2024, pp. 16334-16348.
• RAVDESS: Steven R Livingstone and Frank A Russo, "The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english," PloS one, vol. 13, no. 5, pp. e0196391, 2018.
• CH-SIMSv2.0: Yihe Liu, Ziqi Yuan, Huisheng Mao, Zhiyun Liang, Wanqiuyue Yang, et al., "Make acoustic and visual cues matter: CH-SIMS v2.0 dataset and av-mixup consistent module," in ICMI, 2022, pp. 247-258.
• MEmoR: Guangyao Shen, Xin Wang, Xuguang Duan, Hongzhi Li, and Wenwu Zhu, "MEmoR: A dataset for multimodal emotion reasoning in videos," in ACM MM, 2020, pp. 493-502.

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📊 Data Pipeline and Model Architecture

Our data construction pipeline integrates a unified fine-grained annotation strategy with automated quality control mechanisms across three stages.

Figure 1: (a) Overview of the SABER data pipeline, featuring Raw Data Cleaning, Fine-grained Multimodal Annotation, and Instruction Generation. (b) Training Paradigm: Stage 1 (SED) for sequential grounding and Stage 2 (CA-DPO) for preference alignment in conflicting scenarios.

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🚀 Quick Start & Inference

1. Installation

Please ensure your environment is set up with ms-swift.

git clone [https://github.com/modelscope/ms-swift.git](https://github.com/modelscope/ms-swift.git)
cd ms-swift
pip install -e .

2. Inference Script

Save the following script as infer.sh to run stream inference on your custom JSONL data. Ensure you modify SWIFT_PATH to your local installation directory.

Save the following script to run stream inference on your custom JSONL data:

#!/bin/bash
# Usage: bash infer.sh <model_path> <input_jsonl> <result_path> [gpu_id]
# Example: bash infer.sh MODEL/SABER-LLM data.jsonl result.jsonl 0
#
# Model download URL: [https://huggingface.co/zhaoxiaoxian/SABER-LLM](https://huggingface.co/zhaoxiaoxian/SABER-LLM)
#
# The `input_jsonl` file should be in the following format:
# {"messages": [{"role": "user", "content": "请通过依次完成以下任务，对提供的媒体进行一次全面的多模态分析：\n- 请详细描述视频的场景及其背景信息。\n- 请对这段音频进行详细分析。\n- 请详细分析说话者的面部表情和肢体语言。\n- 最后，请综合以上所有观察结果，对说话者的情绪状态进行全面分析。"}], "audios": ["/path/to/your/audio.wav"], "videos": ["/path/to/your/video.mp4"]}

MODEL_PATH=$1
INPUT_JSONL=$2
RESULT_PATH=$3
GPU_ID=${4:-0}

# TODO: Change this to your local ms-swift path
SWIFT_PATH="/path/to/your/ms-swift" 
cd "$SWIFT_PATH" || exit

CUDA_VISIBLE_DEVICES=$GPU_ID \
VIDEO_MAX_PIXELS=50176 \
FPS_MAX_FRAMES=40 \
MAX_PIXELS=1003520 \
ENABLE_AUDIO_OUTPUT=0 \
swift infer \
    --model "$MODEL_PATH" \
    --stream true \
    --infer_backend pt \
    --write_batch_size -1 \
    --max_new_tokens 4096 \
    --val_dataset "$INPUT_JSONL" \
    --result_path "$RESULT_PATH"

echo "Finished processing $INPUT_JSONL. Results saved to $RESULT_PATH."

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📅 To-Do List

• Release SABER-LLM-7B model weights
• Release the full SABER training dataset
• Quick Start and Inference Example scripts
• Provide automated data annotation scripts

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📖 Citation

If you find our work useful in your research, please consider citing:

@article{zhao2026integrating,
  title={Integrating Fine-Grained Audio-Visual Evidence for Robust Multimodal Emotion Reasoning},
  author={Zhao, Zhixian and Tian, Wenjie and Xie, Lei},
  journal={arXiv preprint arXiv:2601.18321},
  year={2026}
}