Update README.md

README.md

@@ -78,8 +78,8 @@ cd EAR_VAE2
 # Install dependencies
 pip install -r requirements.txt
 
-# Download pretrained weights (coming soon)
-# huggingface-cli download eps-acoustic-revolution-lab/ear-vae2-small --local-dir checkpoints/
+# Download config + pretrained weights from the Hub
+huggingface-cli download earlab/EAR_VAE2 --local-dir checkpoints/
 ```
 
 ## Usage
@@ -87,17 +87,22 @@ pip install -r requirements.txt
 ### Python API
 
 ```python
+import json
 import torch
+from huggingface_hub import hf_hub_download
 from ear_vae2 import EarVAE2
 
-# Load model (full model, with refiner — see configs/ear_vae2.json)
-config = {
-    "C0": 64, "D": 128, "use_vae": True,
-    "refiner": {"type": "banded", "dim": 256, "intermediate_dim": 1024,
-                "num_layers": 12, "layer_norm_eps": 1e-5},
-}
+REPO_ID = "earlab/EAR_VAE2"
+
+# Resolve config and weights from the Hub (cached locally after the first call)
+config_path = hf_hub_download(REPO_ID, "config.json")
+ckpt_path = hf_hub_download(REPO_ID, "weights/ear_vae2.pt")
+
+with open(config_path) as f:
+    config = json.load(f)["model"]["gen"]["config"]
+
 model = EarVAE2(config)
-ckpt = torch.load("ear_vae2.pt", map_location="cpu")
+ckpt = torch.load(ckpt_path, map_location="cpu")
 model.load_state_dict(ckpt["gen"] if "gen" in ckpt else ckpt)
 model.eval().cuda()
 
@@ -111,8 +116,13 @@ reconstructed = reconstructed[:, :, :orig_len]
 
 ### Command Line
 
+Using the `checkpoints/` directory populated in the Installation step:
+
 ```bash
-python inference.py --checkpoint ear_vae2.pt --config configs/ear_vae2.json --input input.wav --output output.wav
+python inference.py \
+  --checkpoint checkpoints/weights/ear_vae2.pt \
+  --config checkpoints/config.json \
+  --input input.wav --output output.wav
 ```