Update hf_transcriber.py

hf_transcriber.py

@@ -45,16 +45,18 @@ class HFTranscriber:
         try:
             # Try to get Hugging Face token from environment
             hf_token = (os.environ.get('HUGGINGFACE_TOKEN') or os.environ.get('HF_TOKEN') or (st.secrets.get('HUGGINGFACE_TOKEN') if 'secrets' in globals() and hasattr(st.secrets, 'get') else None) or (st.secrets.get('HF_TOKEN') if 'secrets' in globals() and hasattr(st.secrets, 'get') else None))
-            
-            # Common parameters for from_pretrained
-            load_kwargs = {
-                'token': hf_token,  # Use 'token' instead of 'use_auth_token' for newer versions
-                'local_files_only': False
-            }
-            
-            # Remove None values to avoid passing None as token
+            if not hf_token:
+                st.sidebar.error("No Hugging Face token found. Using public access (rate limited).Please add it to your environment variables as HUGGINGFACE_TOKEN or HF_TOKEN.")
+            #Configure headers for API requests
+            headers ={}
+            if hf_token:
+                headers['Authorization'] = f'Bearer {hf_token}'
+            #Configure model loading parameters
+            load_kwargs = {'token': hf_token, 'use_auth_token': hf_token, 'local_files_only': False, 'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
+            #Remove None values
             load_kwargs = {k: v for k, v in load_kwargs.items() if v is not None}
             
+            #Rest of model loading code.....
             if self.is_speecht5:
                 # Load SpeechT5 model and processor
                 self.processor = SpeechT5Processor.from_pretrained(