audio-super-resolution
Maintainer: nateraw
46
| 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
audio-super-resolution is a versatile audio super-resolution model developed by Replicate creator nateraw. It is capable of upscaling various types of audio, including music, speech, and environmental sounds, to higher fidelity across different sampling rates. This model can be seen as complementary to other audio-focused models like whisper-large-v3, which focuses on speech recognition, and salmonn, which handles a broader range of audio tasks.
Model inputs and outputs
audio-super-resolution takes in an audio file and generates an upscaled version of the input. The model supports both single file processing and batch processing of multiple audio files.
Inputs
- Input Audio File: The audio file to be upscaled, which can be in various formats.
- Input File List: A file containing a list of audio files to be processed in batch.
Outputs
- Upscaled Audio File: The super-resolved version of the input audio, saved in the specified output directory.
Capabilities
audio-super-resolution can handle a wide variety of audio types, from music and speech to environmental sounds, and it can work with different sampling rates. The model is capable of enhancing the fidelity and quality of the input audio, making it a useful tool for tasks such as audio restoration, content creation, and audio post-processing.
What can I use it for?
The audio-super-resolution model can be leveraged in various applications where high-quality audio is required, such as music production, podcast editing, sound design, and audio archiving. By upscaling lower-quality audio files, users can create more polished and professional-sounding audio content. Additionally, the model's versatility makes it suitable for use in creative projects, content creation workflows, and audio-related research and development.
Things to try
To get started with audio-super-resolution, you can experiment with processing both individual audio files and batches of files. Try using the model on a variety of audio types, such as music, speech, and environmental sounds, to see how it performs. Additionally, you can adjust the model's parameters, such as the DDIM steps and guidance scale, to explore the trade-offs between audio quality and processing time.
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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