resemble-enhance
Maintainer: lucataco
9
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
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Model overview
The resemble-enhance model is an AI-driven audio enhancement tool powered by Resemble AI. It aims to improve the overall quality of speech by performing denoising and enhancement. The model consists of two modules: a denoiser that separates speech from noisy audio, and an enhancer that further boosts the perceptual audio quality by restoring distortions and extending the audio bandwidth. The models are trained on high-quality 44.1kHz speech data to ensure the enhancement of speech with high quality.
Model inputs and outputs
The resemble-enhance model takes an input audio file and several configurable parameters to control the enhancement process. The output is an enhanced version of the input audio file.
Inputs
- input_audio: Input audio file
- solver: Solver to use (default is Midpoint)
- denoise_flag: Flag to denoise the audio (default is false)
- prior_temperature: CFM Prior temperature to use (default is 0.5)
- number_function_evaluations: CFM Number of function evaluations to use (default is 64)
Outputs
- Output: Enhanced audio file(s)
Capabilities
The resemble-enhance model can improve the overall quality of speech by removing noise and enhancing the audio. It can be used to enhance audio recordings with background noise, such as street noise or music, as well as improve the quality of archived speech recordings.
What can I use it for?
The resemble-enhance model can be used in a variety of applications where high-quality audio is required, such as podcasting, voice-over work, or video production. It can also be used to enhance the audio quality of remote meetings or video calls, or to improve the listening experience for people with hearing impairments. Additionally, the model can be used to enhance the audio quality of archived recordings, such as old interviews or lectures.
Things to try
One interesting thing to try with the resemble-enhance model is to experiment with the different configuration parameters, such as the solver, the prior temperature, and the number of function evaluations. By adjusting these parameters, you can fine-tune the enhancement process to achieve the best results for your specific use case.
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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