FlashSR
FlashSR is a 2MB audio super-resolution model based on the HierSpeech++'s upsampler architecture. It upscales 16kHz audio to 48kHz at speeds ranging from 200x to 400x real-time.
Model source
- Kind
- Other
- File size
- 487 KB–1.64 MB
- Version
- vRevision 3e19cc9
- Updated
- Sep 26, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
FlashSR
FlashSR is a 2MB audio super-resolution model based on the HierSpeech++'s upsampler architecture. It upscales 16kHz audio to 48kHz at speeds ranging from 200x to 400x real-time.
Details
- Model Size: 2MB for pytorch version, 500KB for onnx version
- Input Rate: 16kHz
- Output Rate: 48kHz
- Inference Speed: 200x - 400x real-time depending on gpu and dtype
Performance Summary
FlashSR is designed for high-speed frequency reconstruction. It offers a significantly lower computational footprint compared to alternatives such as Resemble-Enhance and ClearerVoice, while maintaining similar output quality.
Benchmark Comparison
| Model | Speed | Size |
|---|---|---|
| FlashSR | 200x - 400x realtime | 2MB/500KB |
| Resemble-Enhance | < 20x realtime | ~700MB+ |
| ClearerVoice | < 20x realtime | ~200MB+ |
Usage
Usage instructions for onnx/pytorch and source code are available on GitHub: https://github.com/ysharma3501/FlashSR
Credits
Thanks to the authors of HierSpeech++ as this was based on it's 48khz upsampler and Xenova for onnx code.
Sources
1 source
Sources
1 sourceVerified Aug 29
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Model artifacts
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Model artifacts
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Source excerpts
2 excerpts--- license: apache-2.0 pipeline_tag: audio-to-audio tags: - pytorch - audio - upsampling --- # FlashSR FlashSR is a 2MB audio super-resolution model based on the HierSpeech++'s upsampler architecture. It upscales 16kHz audio to 48kHz at speeds ranging from 200x to 400x real-time. ### Details * **Model Size:** 2MB for pytorch version, 500KB for onnx version * **Input Rate:** 16kHz * **Output Rate:** 48kHz * **Inference Speed:** 200x - 400x real-time depending on gpu and dtype ### Performance Summary FlashSR is designed for high-speed frequency reconstruction. It offers a significantly lower computational footprint compared to alternatives such as Resemble-Enhance and ClearerVoice, while maintaining similar output quality. ### Benchmark Comparison | Model | Speed | Size | | :--- | :--- | :--- | | **FlashSR** | **200x - 400x realtime** | **2MB/500KB** | | Resemble-Enhance | < 20x realtime | ~700MB+ | | ClearerVoice | < 20x realtime | ~200MB+ | ### Usage Usage instructions for onnx/pytorch and source code are available on GitHub: https://github.com/ysharma3501/FlashSR ### Credits Thanks to the authors of **HierSpeech++** as this was based on it's 48khz upsampler and [Xenova](https://github.com/xenova/) for onnx code.
Source context: 0 downloads · 65 likes · Pipeline audio-to-audio · Repo YatharthS/FlashSR
