Downloads · 30 days
17
13% of all-time downloads
AudioSR-MLX-4bit
aufklarer/AudioSR-MLX-4bit
AudioSR-MLX-4bit is a audio-to-audio model from aufklarer. Use it for the audio-to-audio task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as cc-by-nc-4.0.
- speech-swift — Apple SDK - soniqo.audio — website - blog — blog
All-time downloads
133
Public
Parameters
108M
383 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors383 MB · 100%
Parameter types
How the weights are stored.
U3283.6M · 78%
Base models
Model card
From the Hugging Face model README
AudioSR-MLX-4bit
- speech-swift — Apple SDK
- soniqo.audio — website
- blog — blog
MLX port of AudioSR — a multi-step latent-diffusion audio super-resolution model — quantized to INT4 weight-only for on-device inference on Apple Silicon. Upsamples any-rate input (mono) to 48 kHz with a 200-step DDIM solver (v-prediction
- classifier-free guidance) over an AudioLDM-style VAE latent, then decodes via a HiFi-GAN vocoder. AudioSR is the teacher model that FlashSR was distilled from — FlashSR runs the same architecture in 1 step instead of 200, at the cost of some quality.
Model
| Total parameters | 672 M (VAE 223 M + UNet 258 M + Vocoder 190 M) |
| Diffusion | 200-step DDIM, v-prediction, cosine schedule, η=0 |
| Classifier-free guidance | scale 3.5 (cond + uncond passes per step) |
| Quantization | INT4 weight-only, group size 64, mode mlx_affine_flat |
| Format | MLX safetensors (single combined bundle) |
| Sample rate | 48 kHz mono out (any-rate mono in) |
| Frame length | 10.24 s (491 520 samples) per forward |
| Bundle size | 365 MB on disk |
| Source | haoheliu/audiosr_basic |
Files
| File | Size | Description |
|---|---|---|
model.safetensors | 365 MB | INT4-quantized VAE + UNet + HiFi-GAN weights |
config.json | ~80 KB | Sub-model configs + quantization metadata + per-tensor shape table for dequant-on-load |
The three sub-models share one safetensors file with vae.*, ldm.*, voc.*
key prefixes. config.quantized_shapes records each tensor's pre-flatten
shape so mx.dequantize can rebuild conv weight tensors at load time.
Performance (Apple Silicon, M-series, 10.24 s @ 48 kHz)
| Metric | Value |
|---|---|
| Real-time factor (wall / audio) | 1.87 |
| Load time | 0.06 s |
| SNR vs FP16 reference | +29.2 dB |
| Peak amplitude | 0.500 (renormalised per upstream) |
AudioSR is markedly slower than FlashSR (RTF 1.87 vs FlashSR's 1.10 on the same hardware) because the 200-step DDIM is the inherent bottleneck. Choose AudioSR when the teacher's quality is preferred; choose FlashSR for real-time on-device use.
Usage
from huggingface_hub import snapshot_download
import mlx.core as mx
import numpy as np
import scipy.io.wavfile as wf
from scipy.signal import resample_poly
bundle = snapshot_download("aufklarer/AudioSR-MLX-4bit")
# See https://github.com/soniqo/speech-swift for production usage.
# Toy Python demo (requires the matching MLX AudioSR runtime):
sr, audio = wf.read("lr.wav")
audio = audio.astype(np.float32) / 32767.0
audio_48 = resample_poly(audio, 48000, sr).astype(np.float32)
from audiosr import AudioSR
model = AudioSR(bundle)
hr = model(mx.array(audio_48), steps=200, cfg_scale=3.5, seed=42)
mx.eval(hr)
wf.write("hr.wav", 48000, (np.clip(np.array(hr), -1, 1) * 32767).astype(np.int16))
Source
- Upstream: haoheliu/audiosr_basic
- Paper: AudioSR: Versatile Audio Super-resolution at Scale
- Distilled student (1-step): FlashSR
License
CC-BY-NC 4.0 — inherited from upstream AudioSR weights. Non-commercial use only.
- Task
- Audio-to-Audio
- Library
- mlx
- License
- cc-by-nc-4.0
- Languages
- en
- Created
- May 21, 2026
- Updated
- May 22, 2026