Update app.py

app.py

@@ -6,21 +6,9 @@ import tempfile
 from huggingface_hub import hf_hub_download
 from sam_audio import SAMAudio, SAMAudioProcessor
 
-# --- Initialization ---
-device = "cuda" if torch.cuda.is_available() else "cpu"
-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("ray-006/model-audio").to(device)
-
-# Load your custom weights into the model
-state_dict = torch.load(checkpoint_path, map_location=device)
-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("ray-006/model-audio")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = SAMAudio.from_pretrained("facebook/sam-audio-large").to(device).eval()
+processor = SAMAudioProcessor.from_pretrained("facebook/sam-audio-large")
 
 def separate_audio(audio_path, description, reranking_candidates):
     if audio_path is None or not description: