auto select device

app.py

@@ -17,9 +17,13 @@ import laion_clap
 from inference_utils import prepare_tokenizer, prepare_model, inference
 from data import AudioTextDataProcessor
 
+if torch.cuda.is_available():
+    device = 'cuda:0'
+else:
+    device = 'cpu'
 
 def load_laionclap():
-    model = laion_clap.CLAP_Module(enable_fusion=True, amodel='HTSAT-tiny').cuda()
+    model = laion_clap.CLAP_Module(enable_fusion=True, amodel='HTSAT-tiny').to(device)
     model.load_ckpt(ckpt='630k-audioset-fusion-best.pt')
     model.eval()
     return model
@@ -94,7 +98,7 @@ def compute_laionclap_text_audio_sim(audio_file, laionclap_model, outputs):
         return [0.0] * len(outputs)
     
     audio_data = data.reshape(1, -1)
-    audio_data_tensor = torch.from_numpy(int16_to_float32(float32_to_int16(audio_data))).float().cuda()
+    audio_data_tensor = torch.from_numpy(int16_to_float32(float32_to_int16(audio_data))).float().to(device)
     audio_embed = laionclap_model.get_audio_embedding_from_data(x=audio_data_tensor, use_tensor=True)
 
     text_embed = laionclap_model.get_text_embedding(outputs, use_tensor=True)

src/factory.py

@@ -30,7 +30,11 @@ class CLAP(nn.Module):
     def __init__(self, clap_config):
         super(CLAP, self).__init__()
         self.method = clap_config["method"]
-        device_id = f'cuda:{torch.cuda.current_device()}'
+
+        if torch.cuda.is_available():
+            device = 'cuda:0'
+        else:
+            device = 'cpu'
 
         if self.method == 'laion-clap':
             # https://github.com/LAION-AI/CLAP
@@ -42,7 +46,7 @@ class CLAP(nn.Module):
                 raise NotImplementedError
         
             enable_fusion = 'fusion' in clap_config["model_name"].lower()
-            self.laion_clap = CLAP_Module(enable_fusion=enable_fusion, amodel=amodel, device=device_id)
+            self.laion_clap = CLAP_Module(enable_fusion=enable_fusion, amodel=amodel, device=device)
             self.laion_clap.load_ckpt(ckpt=clap_config["checkpoint"])
             
             for param in self.laion_clap.parameters():
@@ -57,7 +61,7 @@ class CLAP(nn.Module):
                 clap_config["checkpoint"], 
                 config_root=clap_config["config_root"],
                 version=clap_config['model_name'], 
-                use_cuda=True
+                use_cuda=torch.cuda.is_available()
             )
             
             if clap_config['model_name'] in ['2022', '2023']: