rearange files and updateing files

package/transcribe_video.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+import os
+import shutil
+import subprocess
+from pathlib import Path
+from faster_whisper import WhisperModel
+from faster_whisper.utils import format_timestamp
+
+# Prevent OpenMP thread conflicts from crashing the script
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+# 5 minutes per chunk (300 seconds) keeps RAM usage low and stable
+CHUNK_DURATION_SEC = 300 
+
+print("🔧 Initializing C++ Engine...")
+model = WhisperModel(
+    "base", 
+    device="cpu", 
+    compute_type="int8", 
+    cpu_threads=0, # Let CTranslate2 auto-detect safe core counts
+    num_workers=1         
+)
+print("✅ C++ Model loaded successfully.")
+
+
+def extract_audio_and_chunk(video_path: str, output_dir: Path) -> list:
+    """Extracts and splits audio into 5-minute chunks using a single FFmpeg pass."""
+    print("🚀 Extracting and chunking audio with FFmpeg...")
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Segment format output: chunk_000.wav, chunk_001.wav, etc.
+    chunk_pattern = str(output_dir / "chunk_%03d.wav")
+    
+    command = [
+        "ffmpeg", "-y", "-i", video_path,
+        "-vn", "-ac", "1", "-ar", "16000",
+        "-acodec", "pcm_s16le", "-sn", "-map_chapters", "-1",
+        "-f", "segment", "-segment_time", str(CHUNK_DURATION_SEC),
+        chunk_pattern
+    ]
+    
+    result = subprocess.run(command, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE, text=True)
+    if result.returncode != 0:
+        print(f"❌ FFmpeg Error Output:\n{result.stderr}")
+        raise RuntimeError("FFmpeg extraction and chunking failed.")
+        
+    # Return sorted list of generated chunk files
+    return sorted(list(output_dir.glob("chunk_*.wav")))
+
+
+def transcribe_to_srt(video_path: str, force: bool = False):
+    video_path_obj = Path(video_path)
+    srt_path = video_path_obj.with_suffix(".srt")
+    temp_dir = video_path_obj.parent / f"temp_chunks_{video_path_obj.stem}"
+
+    if srt_path.exists():
+        if not force:
+            print(f"❌ Error: Transcription file already exists: {srt_path}")
+            return
+        else:
+            srt_path.unlink()
+
+    try:
+        # Step 1: Split audio into bite-sized pieces
+        audio_chunks = extract_audio_and_chunk(str(video_path_obj), temp_dir)
+        if not audio_chunks:
+            print("❌ Error: No audio chunks were generated.")
+            return
+
+        print(f"📦 Successfully split audio into {len(audio_chunks)} chunks.")
+        print("🎙️ Starting safe chunk-by-chunk transcription...")
+
+        global_segment_index = 1
+        
+        with open(srt_path, "w", encoding="utf-8") as srt_file:
+            for chunk_idx, chunk_path in enumerate(audio_chunks):
+                # Calculate the time offset for the current chunk
+                time_offset = chunk_idx * CHUNK_DURATION_SEC
+                print(f"\n⏳ Processing chunk {chunk_idx + 1}/{len(audio_chunks)} ({chunk_path.name})...")
+                
+                segments_generator, info = model.transcribe(
+                    str(chunk_path), 
+                    beam_size=1,
+                    vad_filter=True,
+                    temperature=0.0
+                )
+                
+                # Consume chunk generator and shift timestamps instantly
+                for segment in segments_generator:
+                    # Shift timestamps relative to the original video timeline
+                    actual_start = segment.start + time_offset
+                    actual_end = segment.end + time_offset
+                    
+                    start_str = format_timestamp(actual_start, always_include_hours=True)
+                    end_str = format_timestamp(actual_end, always_include_hours=True)
+                    
+                    srt_file.write(f"{global_segment_index}\n{start_str} --> {end_str}\n{segment.text.strip()}\n\n")
+                    global_segment_index += 1
+                    
+                # Free up space as we go by deleting the processed chunk
+                chunk_path.unlink()
+
+        print(f"\n✅ All chunks combined! SRT subtitle file saved in: {srt_path}")
+
+    except Exception as e:
+        print(f"\n❌ Execution Error: {e}")
+    finally:
+        # Clean up the temporary folder entirely
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+
+if __name__ == "__main__":
+    target_video = "download/videos/2813112936/2813112936.mp4"
+    if not os.path.exists(target_video):
+        print(f"❌ System Error: Target video file does not exist at path: {target_video}")
+    else:
+        transcribe_to_srt(target_video, force=True)