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license: mit
library_name: mlx
tags:
- audio
- audio-super-resolution
- mlx
base_model: haoheliu/audiosr_basic
---
# AudioSR basic, trimmed
The released [AudioSR](https://github.com/haoheliu/versatile_audio_super_resolution)
`basic` checkpoint, cut down to what super-resolution runs and saved as
safetensors. It is used by IntelligenceKit's `MLXAudioSR` (Swift, MLX) and
by `afm upscale`.
`basic.safetensors`, 2.69 GB, float32, with PyTorch names and layouts. It holds:
- `model.`: the diffusion UNet
- `first_stage_model.`: the VAE and its 48 kHz HiFi-GAN vocoder
- the scale factor and the noise schedule's buffers
These are left out:
- CLAP, which super-resolution doesn't use;
- the EMA copy of the UNet, since inference uses the live weights;
- the condition encoder's VAE, which is identical to `first_stage_model`.
The MLX port casts to float16 at load time.
`convert.py`, the script that made the file:
```sh
python convert.py basic basic.safetensors
```
## Parity
The MLX port matches PyTorch stage by stage, from 76 to 137 dB SNR. It was
tested on a clip low-passed at 6 kHz and scored by LSD against the original
(lower is better):
| | LSD |
|---|---|
| input | 3.88 |
| PyTorch | 1.16 |
| MLX | 1.14 |
|