Videollama2.1 7B AV CoT

🚀 VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

VideoLLaMA 2 is a cutting - edge video - large language model that enhances spatial - temporal modeling and audio understanding, offering powerful solutions for video and audio - visual question - answering tasks.

Metadata

VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

If you like our project, please give us a star ⭐ on Github for the latest update.

📰 News

• [2024.10.22] Release checkpoints of VideoLLaMA2.1 - 7B - AV
• [2024.10.15] Release checkpoints of VideoLLaMA2.1 - 7B - 16F - Base and VideoLLaMA2.1 - 7B - 16F
• [2024.08.14] Release checkpoints of VideoLLaMA2 - 72B - Base and VideoLLaMA2 - 72B
• [2024.07.30] Release checkpoints of VideoLLaMA2 - 8x7B - Base and VideoLLaMA2 - 8x7B.
• [2024.06.25] 🔥🔥 As of Jun 25, our VideoLLaMA2 - 7B - 16F is the Top - 1 ~7B - sized VideoLLM on the MLVU Leaderboard.
• [2024.06.18] 🔥🔥 As of Jun 18, our VideoLLaMA2 - 7B - 16F is the Top - 1 ~7B - sized VideoLLM on the VideoMME Leaderboard.
• [2024.06.17] 👋👋 Update technical report with the latest results and the missing references. If you have works closely related to VideoLLaMA 2 but not mentioned in the paper, feel free to let us know.
• [2024.06.14] 🔥🔥 Online Demo is available.
• [2024.06.03] Release training, evaluation, and serving codes of VideoLLaMA 2.

🌎 Model Zoo

Vision - Only Checkpoints

Audio - Visual Checkpoints

🚀 Main Results

Multi - Choice Video QA & Video Captioning

Open - Ended Video QA

Multi - Choice & Open - Ended Audio QA

Open - Ended Audio - Visual QA

💻 Usage Examples

Basic Usage

import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init
import argparse

def inference(args):

    model_path = args.model_path
    model, processor, tokenizer = model_init(model_path)

    if args.modal_type == "a":
        model.model.vision_tower = None
    elif args.modal_type == "v":
        model.model.audio_tower = None
    elif args.modal_type == "av":
        pass
    else:
        raise NotImplementedError
    # Audio-visual Inference
    audio_video_path = "assets/00003491.mp4"
    preprocess = processor['audio' if args.modal_type == "a" else "video"]
    if args.modal_type == "a":
        audio_video_tensor = preprocess(audio_video_path)
    else:
        audio_video_tensor = preprocess(audio_video_path, va=True if args.modal_type == "av" else False)
    question = f"Please describe the video with audio information."

    # Audio Inference
    audio_video_path = "assets/bird-twitter-car.wav"
    preprocess = processor['audio' if args.modal_type == "a" else "video"]
    if args.modal_type == "a":
        audio_video_tensor = preprocess(audio_video_path)
    else:
        audio_video_tensor = preprocess(audio_video_path, va=True if args.modal_type == "av" else False)
    question = f"Please describe the audio."

    # Video Inference
    audio_video_path = "assets/output_v_1jgsRbGzCls.mp4"
    preprocess = processor['audio' if args.modal_type == "a" else "video"]
    if args.modal_type == "a":
        audio_video_tensor = preprocess(audio_video_path)
    else:
        audio_video_tensor = preprocess(audio_video_path, va=True if args.modal_type == "av" else False)
    question = f"What activity are the people practicing in the video?"

    output = mm_infer(
        audio_video_tensor,
        question,
        model=model,
        tokenizer=tokenizer,
        modal='audio' if args.modal_type == "a" else "video",
        do_sample=False,
    )

    print(output)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument('--model-path', help='', required=False, default='DAMO-NLP-SG/VideoLLaMA2.1-7B-AV')
    parser.add_argument('--modal-type', choices=["a", "v", "av"], help='', required=True)
    args = parser.parse_args()

    inference(args)

📄 License

If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:

@article{damonlpsg2024videollama2,
  title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
  author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
  journal={arXiv preprint arXiv:2406.07476},
  year={2024},
  url = {https://arxiv.org/abs/2406.07476}
}

@article{damonlpsg2023videollama,
  title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
  author = {Zhang, Hang and Li, Xin and Bing, Lidong},
  journal = {arXiv preprint arXiv:2306.02858},
  year = {2023},
  url = {https://arxiv.org/abs/2306.02858}
}