vta-ldm
V
Vta Ldm
Overview :
vta-ldm is a deep learning model focused on video-to-audio generation. It can generate audio content semantically and temporally aligned with the video input. It represents a new breakthrough in the field of video generation, especially following the significant progress made in text-to-video generation technology. Developed by Manjie Xu and others at the Tencent AI Lab, the model has the ability to generate audio that is highly consistent with video content, and has important application value in video production, audio post-processing, and other fields.
Target Users :
This product is suitable for video producers, audio engineers, and any professional who needs to generate audio based on video content. It can help them quickly generate audio that matches the video content, improving work efficiency, and adding a richer and more engaging auditory experience to the video.
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Use Cases
Adding background music or dialogue to silent videos in video production
Generating ambient sounds based on video scenes in audio post-production
Automatically generating narration audio for educational videos
Features
Generate semantically and temporally aligned audio based on video content
Support installation using conda to install Python dependencies
Provide recommended methods for downloading and checking checkpoints from huggingface
Provide multiple model variants, such as VTA_LDM+IB/LB/CAVP/VIVIT, etc.
Allow users to customize hyperparameters to meet individual needs
Provide scripts to help merge the generated audio with the original video
Audio and video merging function based on ffmpeg
How to Use
1. Install a Python environment and use conda to install the required dependency packages.
2. Download the model checkpoint from huggingface.
3. Place the video files in the designated data directory.
4. Run the provided inference script to start generating audio content from the input video.
5. Adjust the hyperparameters in the script as needed.
6. Use the provided script to merge the generated audio with the original video.
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