What is CogSound
CogSound is Zhipu AI's latest sound effect model, capable of adding moving sound effects to silent videos. Based onGLM-4V's video understanding capabilities, CogSound can accurately recognize and understand the semantics and emotions behind videos, adding matching audio content to silent videos. It can generate more complex sound effects, such as explosions, water flows, musical instruments, animal calls, vehicle sounds, etc. The launch of the model marks Zhipu AI's technological progress in the field of video generation, especially in enhancing the multimodal experience of videos and strengthening their immersion and realism.
CogSound's main features
- Generate sound effects matching the visuals：CogSound can generate sound effects matching the visuals for videos, providing a richer audiovisual experience.
- Supports 4K ultra-high-definition video generation：Supports generating 10-second, 4K-resolution, 60-frame ultra-high-definition videos, while matching the corresponding sound effects.
- Adapts to different playback needs：Supports video generation in any aspect ratio to adapt to different playback needs, and generates matching sound effects for these videos.
- Multi-channel video generation：The same instruction/image can generate 4 videos at once, and each video has corresponding sound effects.
- Enhance the video generation experience：By adding sound effects, CogSound enhances the immersion and realism of video content, making the video generation experience more complete and vivid.
- Sound effect feature public beta：CogSound's sound effect feature will soon launch in public beta (expected at the end of November), and users will be able to experience it in Zhipu Qingying the sound effect generation service provided by CogSound.
CogSound's technical features
- Unet-based latent space diffusion：
- Efficient audio generation: CogSound is based on the Latent Diffusion Model, transferring the audio generation process from a high-dimensional original space to a low-dimensional latent space, which helps reduce computational complexity.
- Optimized U-Net structure: As the core framework of the diffusion model, the U-Net structure has been optimized to improve the performance of the audio synthesis process while maintaining the high quality and efficiency of the generated audio.
- Block-wise temporal alignment cross-attention：
- Strengthen the correlation between audio and video features: By introducing the Block-wise Temporal Alignment Cross-attention mechanism, CogSound can optimize feature matching between long video sequences and audio features.
- Precise audio-video mapping: By learning the relationship between frame-level video features and audio features, it achieves precise audio-video mapping, ensuring that every frame of the picture can find its place in the notes, and every note can accurately echo in the video.
- Rotary position encoding：
- Improve temporal modeling accuracy: CogSound integrates rotary position encoding technology to provide a unique identifier for each position in the sequence and capture the relative relationships between positions, helping to improve temporal consistency.
- Coherence and naturalness of transitions: Rotary position encoding ensures the coherence and naturalness of transitions in audio sequences, and can avoid "discontinuities" or "misalignments" in audio generation when handling long temporal tasks.
CogSound's application scenarios
- Video content creation: Provide video content creators with a richer selection of sound effects, enhancing the expressiveness of videos.
- Advertising Production: Add matching sound effects to advertising videos to increase the appeal and memorability of the ads.
- Film and Television Post-Production: In film and television post-production, provide corresponding sound effect support for the visuals, improving production efficiency and quality.