Upload generate_caption.py

generate_caption.py

@@ -0,0 +1,67 @@
+import os
+import time 
+from pathlib import Path
+import torch
+from transformers import pipeline
+torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+
+    folder_path = "./part_1"
+
+    for roots, dirs, files in os.walk(folder_path):
+        for file in files:
+
+            if file.lower().endswith('.en.srt') | file.lower().endswith('.en-orig.srt'):
+                print('[srt] file are found.')
+
+                
+            else:
+                if file.lower().endswith('.mp4'):
+                    video_path = Path(roots+'/'+file)
+                    
+                    if video_path.exists():
+                        split_video_path = str(video_path).split('.mp4')[-2]
+                        create_srt_path = split_video_path.__add__('.en.srt')
+                        create_srt_path = Path(create_srt_path)
+                        # if create_srt_path.exists() and create_srt_path.read_text(encoding="utf-8"):
+                        if create_srt_path.exists() and create_srt_path.stat().st_size > 0:
+                            # content = create_srt_path.read_text(encoding="utf-8")
+                            print(f"content are already found in [srt] file. filepath: >>>> {create_srt_path}")
+                            
+                        else:
+                            
+
+                            # Initialize the pipeline (handles chunking intelligently)
+                            device = "cuda" if torch.cuda.is_available() else "cpu"
+                            pipe = pipeline(
+                                "automatic-speech-recognition",
+                                model="openai/whisper-large-v3",
+                                torch_dtype=torch.float16,
+                                device=device,
+                            )
+
+                            # Generate with built-in chunking and stride
+                            # chunk_length_s=30: Process 30s at a time
+                            # stride_length_s=5: Overlap chunks by 5s to connect sentences correctly
+                            outputs = pipe(
+                                str(video_path),
+                                chunk_length_s=30,
+                                stride_length_s=5,
+                                generate_kwargs={"task": "translate", "language": "hindi"}
+                            )
+
+                            # print(outputs["text"])
+                            print(">>>>>>>>>>>>>>>>>>> :", create_srt_path)
+
+                            torch.cuda.empty_cache()
+
+                            # Save it so you don't lose it!
+                            with open(create_srt_path, "w") as f:
+                                f.write(outputs["text"])
+
+                    else:
+                        print('video_path are not working...')
+
+            # ---------------------------------------------------------------