Update app.py

app.py

@@ -12,7 +12,7 @@ REPO_ID = "ray-006/model-audio"
 checkpoint_path = hf_hub_download(repo_id=REPO_ID, filename="checkpoint.pt")
 
 # 4. Load the model and apply your weights
-model = SAMAudio.from_pretrained("facebook/sam-audio-large").to(device)
+model = SAMAudio.from_pretrained("ray-006/model-audio").to(device)
 
 # Load your custom weights into the model
 state_dict = torch.load(checkpoint_path, map_location=device)
@@ -20,7 +20,7 @@ model.load_state_dict(state_dict)
 model.eval()
 
 # Load processor (can be from the base or your repo if you uploaded config there)
-processor = SAMAudioProcessor.from_pretrained("facebook/sam-audio-large")
+processor = SAMAudioProcessor.from_pretrained("ray-006/model-audio")
 
 def separate_audio(audio_path, description, reranking_candidates):
     if audio_path is None or not description: