Audio Super Resolution
The audio-super-resolution AI model can be effectively used across several domains that extensively work with audio data. In the music industry, for example, the model can improve low-resolution audio files by advancing the quality of sound, ensuring a better listening experience for audiences. Similarly, this technology can be leveraged by streaming platforms or radio stations for broadcasting high-quality audio content. Podcast creators can also utilize the tool to enhance the quality of their recordings, ensuring clarity and crispness in their final distribution. Other sectors like military and law enforcement could use the model to increase the intelligibility of critical audio content extracted from surveillance or communications. Companies providing audio transcription services can also benefit from super-resolution audio to achieve more accurate transcriptions. The model can be adapted into products like audio editing software, transcription tools, or integrated into existing platforms for self-improvement capabilities. This technology has extensive potential to revolutionize how we interact with and consume audio content.
- Cost per run
- Avg run time
|Llama 2 70b Chat Awq
|Stablecode Completion Alpha 3b 4k
|Samsum Llama 7b
|Whisper Large V3
You can use this area to play around with demo applications that incorporate the Audio Super Resolution model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Audio Super Resolution
AudioSR: Versatile Audio Super-resolution at Scale
|View on Replicate
|View on Replicate
|View on Github
|View on Arxiv
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run
|Average Completion Time