The Zero Shot Audio Source Separation model has a wide range of potential use cases in various industries. In music production, it can be used to isolate individual instruments or vocals from a mixed recording, allowing for remixing or enhancing specific elements in a song. In audio transcription and speech recognition, it can help separate different speakers or remove background noise, improving the accuracy of transcriptions or voice commands. In surveillance or security applications, it can be used to extract and analyze specific sounds from audio recordings, such as detecting gunshots or breaking glass. In the gaming industry, it can enhance user experience by separating different audio sources in immersive environments, making the virtual world more realistic. The possibilities for practical applications of this model are vast, and it has the potential to revolutionize audio processing and analysis across industries.
- Cost per run
- Avg run time
- Nvidia T4 GPU
You can use this area to play around with demo applications that incorporate the Zero_shot_audio_source_separation 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.
Zero shot Sound separation by arbitrary query samples
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||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||$0.0165|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||30 seconds|