wsrglow
Maintainer: zkx06111
1
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| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
wsrglow is a Glow-based waveform generative model for audio super-resolution, developed by the researcher zkx06111. It can intelligently upsample audio by 2x resolution, similar to models like AudioSR and ARBSR. The model is based on the Interspeech 2021 paper [object Object].
Model inputs and outputs
wsrglow takes a low-sample rate audio file in WAV format as input and generates a high-resolution version of the same audio. The input and output files can be used for audio upsampling tasks.
Inputs
- input: Low-sample rate input file in .wav format
Outputs
- file: High-resolution output file in .wav format
- text: (not used)
Capabilities
wsrglow can intelligently upscale audio by 2x resolution, preserving details and maintaining audio quality. It leverages Glow, a powerful generative model, to achieve this. The model is capable of handling a variety of audio content, from speech to music.
What can I use it for?
The wsrglow model can be useful for a range of audio processing applications that require high-quality upsampling, such as enhancing the resolution of audio recordings, improving the fidelity of music tracks, or processing low-quality speech samples. It could be particularly valuable in scenarios where audio quality is important, like content production, audio engineering, or multimedia applications.
Things to try
Experiment with different types of audio inputs, from speech to music, to see how wsrglow performs. You can also try varying the input resolution to observe the model's upscaling capabilities. Additionally, you could explore ways to integrate wsrglow into your own audio processing pipelines or workflows.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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