Update model card with full description, usage, and attribution

README.md

@@ -1,3 +1,92 @@
 ---
 license: mit
+library_name: mlx
+tags:
+  - mlx
+  - audio
+  - speech-enhancement
+  - noise-suppression
+  - deepfilternet
+  - apple-silicon
+base_model: DeepFilterNet/DeepFilterNet3
+pipeline_tag: audio-to-audio
 ---
+
+# DeepFilterNet3 — MLX
+
+MLX-compatible weights for [DeepFilterNet3](https://github.com/Rikorose/DeepFilterNet), a real-time speech enhancement model that suppresses background noise from audio.
+
+This is a direct conversion of the original PyTorch weights to `safetensors` format for use with [MLX](https://github.com/ml-explore/mlx) on Apple Silicon.
+
+## Origin
+
+- **Original model:** [DeepFilterNet3](https://github.com/Rikorose/DeepFilterNet) by Hendrik Schröter
+- **Paper:** [DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement](https://arxiv.org/abs/2305.08227)
+- **License:** MIT (same as the original)
+- **Conversion:** PyTorch → `safetensors` via the included `convert_deepfilternet.py` script
+
+No fine-tuning or quantisation was applied — the weights are numerically identical to the original checkpoint.
+
+## Files
+
+| File | Description |
+|---|---|
+| `config.json` | Model architecture configuration |
+| `model.safetensors` | Pre-converted weights (8.3 MB, float32) |
+| `convert_deepfilternet.py` | Conversion script (PyTorch → MLX safetensors) |
+
+## Model Details
+
+| Parameter | Value |
+|---|---|
+| Sample rate | 48 kHz |
+| FFT size | 960 |
+| Hop size | 480 |
+| ERB bands | 32 |
+| DF bins | 96 |
+| DF order | 5 |
+| Parameters | ~2M |
+
+## Usage
+
+### Swift (mlx-audio-swift)
+
+```swift
+import MLXAudioSTS
+
+let model = try await DeepFilterNetModel.fromPretrained("iky1e/DeepFilterNet3-MLX")
+let enhanced = try model.enhance(audioArray)
+```
+
+### Python (mlx-audio)
+
+```python
+from mlx_audio.sts.models.deepfilternet import DeepFilterNetModel
+
+model = DeepFilterNetModel.from_pretrained(version=3, model_dir="path/to/local/dir")
+enhanced = model.enhance("noisy.wav")
+```
+
+## Converting from PyTorch
+
+To re-create this conversion from the original DeepFilterNet checkpoint:
+
+```bash
+python convert_deepfilternet.py \
+  --input /path/to/DeepFilterNet3 \
+  --output ./DeepFilterNet3-MLX \
+  --name DeepFilterNet3
+```
+
+The input directory should contain a `config.ini` and a `checkpoints/` folder from the original repo.
+
+## Citation
+
+```bibtex
+@inproceedings{schroeter2023deepfilternet3,
+  title={DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement},
+  author={Schr{\"o}ter, Hendrik and Rosenkranz, Tobias and Maier, Andreas and Escalante-B., Alberto N.},
+  booktitle={INTERSPEECH},
+  year={2023}
+}
+```