LavaSR
LavaSR(v2) is a novel 50MB BWE(bandwidth extension) model along with the UL-UNAS denoiser. It can enhance nearly 5000 seconds of audio in just 1 second while exceeding the quality of 6gb large diffusion models.
Model source
- Kind
- Other
- File size
- 784 KB–53.7 MB
- Version
- vRevision b98dc8b
- Updated
- Oct 3, 2026
- Downloads
- 898
- Source
- Hugging Face
Source description
LavaSR(v2) is a novel 50MB BWE(bandwidth extension) model along with the UL-UNAS denoiser. It can enhance nearly 5000 seconds of audio in just 1 second while exceeding the quality of 6gb large diffusion models.
Details
- Model Size: 50mb for pytorch version.
- Input Rate: Any from 8-48khz.
- Output Rate: 48kHz
- Inference Speed: 20-80x realtime on CPU and 800-5000x realtime depending on GPU.
Use cases
- Restore low quality audio datasets
- Enhance TTS or ASR model quality.
- Upscale poor quality voice calls.
Benchmark Comparison
Please check out the repo for objective benchmarks: https://github.com/ysharma3501/LavaSR
| Model | Speed on GPU(bs=1) | Size | Input range | Quality |
|---|---|---|---|---|
| LavaSR v2 | 5000x | 50MB | Any from 8-48khz | Highest |
| AudioSR | < 1x realtime | ~3gb+ | ~2-16khz | Medium |
| AP-BWE(previous formal fastest) | < 400x realtime | ~200MB+ | 8khz/12khz/16khz | High |
| NovaSR(previous informal fastest) | <3600x realtime | ~50KB+ | 16khz | Low |
Usage
Usage instructions can be found here: https://github.com/ysharma3501/LavaSR
Final notes
The model and code are licensed under the Apache-2.0 license. See LICENSE for details.
Stars/Likes would be appreciated, thank you.
Email: yatharthsharma3501@gmail.com
Sources
1 source
Sources
1 sourceVerified Aug 22
Model artifacts
3 artifacts
Model artifacts
3 artifactsSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 pipeline_tag: audio-to-audio --- LavaSR(v2) is a novel 50MB BWE(bandwidth extension) model along with the UL-UNAS denoiser. It can enhance nearly 5000 seconds of audio in just 1 second while exceeding the quality of 6gb large diffusion models. ### Details * **Model Size:** 50mb for pytorch version. * **Input Rate:** Any from 8-48khz. * **Output Rate:** 48kHz * **Inference Speed:** 20-80x realtime on CPU and 800-5000x realtime depending on GPU. ### Use cases - Restore low quality audio datasets - Enhance TTS or ASR model quality. - Upscale poor quality voice calls. ### Benchmark Comparison Please check out the repo for objective benchmarks: https://github.com/ysharma3501/LavaSR | Model | Speed on GPU(bs=1) | Size | Input range| Quality | | :--- | :--- | :--- | :--- | :--- | | **LavaSR v2** | **5000x** | **50MB** | **Any from 8-48khz** | **Highest** | | AudioSR | < 1x realtime | ~3gb+ | ~2-16khz | Medium | | AP-BWE(previous formal fastest) | < 400x realtime | ~200MB+ | 8khz/12khz/16khz | High | | NovaSR(previous informal fastest) | <3600x realtime | ~50KB+ | 16khz | Low | ### Usage Usage instructions can be found here: https://github.com/ysharma3501/LavaSR ### Final notes The model and code are licensed under the Apache-2.0 license. See LICENSE for details. Stars/Likes would be appreciated, thank you. Email: yatharthsharma3501@gmail.com
Source context: 898 downloads · 84 likes · Pipeline audio-to-audio · Repo YatharthS/LavaSR
