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README.md

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 ---
 license: cc-by-nc-4.0
+language:
+  - en
+tags:
+  - audio
+  - audio-to-audio
+  - speech
+  - declipping
+  - audio-restoration
+  - flow-matching
+pipeline_tag: audio-to-audio
+library_name: pytorch
 ---
+
+# audio-restore
+
+Repairs speech recordings damaged by clippings. A
+generative model reconstructs the samples that were destroyed.
+
+## Usage
+
+```bash
+git clone https://github.com/tdstt22/audio-restore
+cd audio-restore && uv sync
+python restore.py recording.wav restored.wav
+```
+
+## Components
+
+| File | Description |
+|---|---|
+| `model.pt` | 2D U-Net, 48.9M params — flow-matching velocity predictor over complex STFT. EMA weights plus architecture config |
+
+The model works on spectrograms at 24 kHz mono. It is given the damaged audio
+and a mask marking which samples the clipping destroyed, so it never has to
+guess where the damage is. Rather than emitting audio directly, it predicts the
+direction from noise toward clean speech, and sampling follows that direction
+over to arrive at the reconstruction.
+
+## Limitations
+
+- **24 kHz** — higher-rate files are converted in and out, so repaired regions
+  carry nothing above 12 kHz. Audible as dullness on sibilants.
+- **Speech only** — music and general audio are out of distribution.
+- **Hard clipping only** — only focused on hard clippings that require regeneration
+- **Degradations above ~30% clipped** — did not optimize for these situations during training

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62c0a5c318ffc26d3023d6a67d48cab73718bac613104739970b00ca731298a0
+size 195700955