--- license: apache-2.0 datasets: - lmms-lab/ClothoAQA - Loie/VGGSound language: - en metrics: - accuracy pipeline_tag: visual-question-answering library_name: transformers tags: - Audio-visual Question Answering - Audio Question Answering - multimodal large language model ---

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

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## 📰 News * **[2024.10.22]** Release checkpoints of [VideoLLaMA2.1-7B-AV](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-AV) * **[2024.10.15]** Release checkpoints of [VideoLLaMA2.1-7B-16F-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base) and [VideoLLaMA2.1-7B-16F](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-16F) * **[2024.08.14]** Release checkpoints of [VideoLLaMA2-72B-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-72B-Base) and [VideoLLaMA2-72B](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-72B) * **[2024.07.30]** Release checkpoints of [VideoLLaMA2-8x7B-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-8x7B-Base) and [VideoLLaMA2-8x7B](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-8x7B). * **[2024.06.25]** 🔥🔥 As of Jun 25, our [VideoLLaMA2-7B-16F](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-7B-16F) is the **Top-1** ~7B-sized VideoLLM on the [MLVU Leaderboard](https://github.com/JUNJIE99/MLVU?tab=readme-ov-file#trophy-mini-leaderboard). * **[2024.06.18]** 🔥🔥 As of Jun 18, our [VideoLLaMA2-7B-16F](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-7B-16F) is the **Top-1** ~7B-sized VideoLLM on the [VideoMME Leaderboard](https://video-mme.github.io/home_page.html#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](https://huggingface.co./spaces/lixin4ever/VideoLLaMA2) is available. * **[2024.06.03]** Release training, evaluation, and serving codes of VideoLLaMA 2. ## 🌎 Model Zoo ### Vision-Only Checkpoints | Model Name | Type | Visual Encoder | Language Decoder | # Training Frames | |:-------------------|:--------------:|:----------------|:------------------|:----------------------:| | [VideoLLaMA2-7B-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-7B-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) | 8 | | [VideoLLaMA2-7B](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-7B) | Chat | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) | 8 | | [VideoLLaMA2-7B-16F-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-7B-16F-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) | 16 | | [VideoLLaMA2-7B-16F](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-7B-16F) | Chat | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) | 16 | | [VideoLLaMA2-8x7B-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-8x7B-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co./mistralai/Mixtral-8x7B-Instruct-v0.1) | 8 | | [VideoLLaMA2-8x7B](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-8x7B) | Chat | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co./mistralai/Mixtral-8x7B-Instruct-v0.1) | 8 | | [VideoLLaMA2-72B-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-72B-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Qwen2-72B-Instruct](https://huggingface.co./Qwen/Qwen2-72B-Instruct) | 8 | | [VideoLLaMA2-72B](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2-72B) | Chat | [clip-vit-large-patch14-336](https://huggingface.co./openai/clip-vit-large-patch14-336) | [Qwen2-72B-Instruct](https://huggingface.co./Qwen/Qwen2-72B-Instruct) | 8 | | [VideoLLaMA2.1-7B-16F-Base](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base) | Base | [siglip-so400m-patch14-384](https://huggingface.co./google/siglip-so400m-patch14-384) | [Qwen2-7B-Instruct](https://huggingface.co./Qwen/Qwen2-7B-Instruct) | 16 | | [VideoLLaMA2.1-7B-16F](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-16F) | Chat | [siglip-so400m-patch14-384](https://huggingface.co./google/siglip-so400m-patch14-384) | [Qwen2-7B-Instruct](https://huggingface.co./Qwen/Qwen2-7B-Instruct) | 16 | ### Audio-Visual Checkpoints | Model Name | Type | Audio Encoder | Language Decoder | |:-------------------|:--------------:|:----------------|:----------------------:| | [VideoLLaMA2.1-7B-AV](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-AV) (**This Checkpoint**) | Chat | [Fine-tuned BEATs_iter3+(AS2M)(cpt2)](https://1drv.ms/u/s!AqeByhGUtINrgcpj8ujXH1YUtxooEg?e=E9Ncea) | [VideoLLaMA2.1-7B-16F](https://huggingface.co./DAMO-NLP-SG/VideoLLaMA2.1-7B-16F) | ## 🚀 Main Results ### Multi-Choice Video QA & Video Captioning

### Open-Ended Video QA

### Multi-Choice & Open-Ended Audio QA

### Open-Ended Audio-Visual QA

## 🤖 Inference with VideoLLaMA2-AV ```python 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) ``` ## Citation If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX: ```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} } ```