A Glow-based Waveform Generative Model for Audio Super-Resolution. Intelligently upsamples audio by 2x resolution
😵 Uh oh! This model can't be run on Replicate because it was built with a version of Cog or Python that is no longer supported. Consider opening an issue on the model's GitHub repository to see if it can be updated to use a recent version of Cog. If you need any help, please Contact us about it.
This model costs approximately $0.0021 to run on Replicate, or 476 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 10 seconds. The predict time for this model varies significantly based on the inputs.
This is an interactive demo of the INTERSPEECH 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution by Kexun Zhang, Yi Ren, Changliang Xu, and Zhou Zhao.
Additional audio samples can be found here: https://zkx06111.github.io/wsrglow/