Change CUDA to CPU

app.py

@@ -23,8 +23,8 @@ from face_detection_utils import detect_faces
 # Use HF Space's temp directory
 TEMP_DIR = os.environ.get('TMPDIR', '/tmp')
 
-# Shared state for relaying GPU-side status back to the UI thread.
-GPU_PROGRESS_STATE = {"progress": 0.0, "status": "Processing on GPU..."}
+# Shared state for relaying CPU-side status back to the UI thread.
+GPU_PROGRESS_STATE = {"progress": 0.0, "status": "Processing on CPU..."}
 GPU_PROGRESS_LOCK = threading.Lock()
 
 class LogCollector:
@@ -184,10 +184,9 @@ def process_detected_faces(boxes, probs, frame_rgb, frame_pil):
     full_frame_annotated = np.array(full_frame_pil)
     return face_images, len(boxes), full_frame_annotated, None
 
-@spaces.GPU(duration=60, enable_queue=True)
 def detect_faces_gpu(frame_pil):
-    """GPU-accelerated face detection"""
-    print("Detecting faces with RetinaFace")
+    """CPU face detection"""
+    print("Detecting faces with RetinaFace (CPU)")
 
     frame_array = np.array(frame_pil)
 
@@ -271,16 +270,15 @@ def detect_and_extract_all_faces(video_path):
         traceback.print_exc()
         return [], 0, None, f"Failed to open video file. Error: {str(e)}"
 
-@spaces.GPU(duration=300, enable_queue=True)
 def process_video_gpu(video_file, temp_dir, num_speakers):
-    """GPU-accelerated video processing"""
+    """CPU video processing"""
     try:
         from Inference_with_status import process_video_with_status
         
         # Define status callback inside GPU function
         def gpu_status_callback(message):
             status_text = message.get('status', 'Processing...')
-            print(f"GPU Processing: {status_text}")
+            print(f"CPU Processing: {status_text}")
             progress_value = message.get('progress')
             with GPU_PROGRESS_LOCK:
                 GPU_PROGRESS_STATE["status"] = status_text
@@ -391,13 +389,13 @@ def process_video_auto(video_file, progress=gr.Progress()):
             yield snapshot()
 
             try:
-                print("Starting GPU-accelerated video processing...")
+                print("Starting CPU video processing...")
                 with GPU_PROGRESS_LOCK:
                     GPU_PROGRESS_STATE["progress"] = 0.0
-                    GPU_PROGRESS_STATE["status"] = "Processing on GPU..."
+                    GPU_PROGRESS_STATE["status"] = "Processing on CPU..."
 
-                progress(0.4, desc="Processing on GPU...")
-                status_value = "Processing on GPU..."
+                progress(0.4, desc="Processing on CPU...")
+                status_value = "Processing on CPU..."
                 yield snapshot()
 
                 gpu_result = {"output_files": None, "exception": None}
@@ -416,12 +414,12 @@ def process_video_auto(video_file, progress=gr.Progress()):
                 gpu_thread.start()
 
                 last_reported_progress = 0.4
-                last_status_message = "Processing on GPU..."
+                last_status_message = "Processing on CPU..."
 
                 while gpu_thread.is_alive():
                     time.sleep(0.5)
                     with GPU_PROGRESS_LOCK:
-                        gpu_status = GPU_PROGRESS_STATE.get("status", "Processing on GPU...")
+                        gpu_status = GPU_PROGRESS_STATE.get("status", "Processing on CPU...")
                         gpu_progress_value = GPU_PROGRESS_STATE.get("progress", 0.0)
 
                     mapped_progress = 0.4 + 0.5 * gpu_progress_value
@@ -632,7 +630,7 @@ with gr.Blocks(
         ### 🚀 Powered by:
         - RetinaFace for face detection
         - Dolphin model for audio-visual separation
-        - GPU acceleration when available
+        - CPU processing
         <footer style="display:none;">
             <a href='https://clustrmaps.com/site/1c828' title='Visit tracker'>
                 <img src='//clustrmaps.com/map_v2.png?cl=080808&w=300&t=tt&d=XYmTC4S_SxuX7G06iJ16lU43VCNkCBFRLXMfEM5zvmo&co=ffffff&ct=808080'/>

look2hear/models/dolphin.py

@@ -1256,13 +1256,13 @@ class Dolphin(nn.Module, PyTorchModelHubMixin):
         cls,
         *,
         model_id: str,
-        revision: str,
-        cache_dir: str,
-        force_download: bool,
-        proxies: dict,
-        resume_download: bool,
-        local_files_only: bool,
-        token: str,
+        revision: str = None,
+        cache_dir: str = None,
+        force_download: bool = False,
+        proxies: dict = None,
+        resume_download: bool = False,
+        local_files_only: bool = False,
+        token: str = None,
         map_location: str = "cpu",
         strict: bool = False,
         **model_kwargs,