DeepSound-V1 is a framework designed for the generation of high-quality, synchronized audio from video and optional text inputs. This technology leverages multi-modal joint learning frameworks to achieve precise alignment between visual and audio domains. One of the key challenges in audio generation from videos is the lack of sufficient temporal and semantic alignment annotations in existing benchmarks. DeepSound-V1 addresses this by utilizing the internal chain-of-thought (CoT) of a multi-modal large language model (MLLM) to enable step-by-step reasoning without requiring additional annotations. The framework constructs a multi-modal reasoning dataset to facilitate the learning of initial reasoning in audio generation. By reducing misalignment in generated audio, DeepSound-V1 achieves competitive performance compared to state-of-the-art models. The evaluation results demonstrate significant improvements in various performance metrics, highlighting the effectiveness of the proposed framework in audio generation tasks.
Multi-modal joint learning frameworks
Multi-modal large language model (MLLM)
Multi-modal reasoning dataset
F DP aSST, F DP AN N s, F DV GG, IS indicator, IB-score, DeSync indicator
Cloud-based, on-premises
Yes
Yes
Synchronized audio generation, multi-modal reasoning
Yes
High-performance GPUs for model training and inference
Windows, Linux
Can integrate with video editing software
Data encryption, access control
Varies by application
Varies by implementation
No
Active research community
Audio engineers, data scientists
Large multi-modal datasets
Low latency for real-time audio generation
Optimized for GPU usage
Model interpretability tools
Copyright, data privacy
Limited by the quality of input data
Media, entertainment
Audio generation, video editing
Media companies, content creators
APIs, SDKs
Scalable with cloud resources
Technical support, consulting services
Varies by provider
Web-based dashboards, APIs
Yes
Language localization options
Subscription, pay-per-use
Yes
Technology partners, academic collaborations
Varies by implementation
Complies with industry regulations
Varies by implementation
SaaS, PaaS
Yes
RESTful APIs, SDKs
B2B, B2C
0.00
USD
Commercial, open-source
Unknown
Unknown
Continuous learning, adaptive algorithms
Yes