Cleaned

video_to_text.py

@@ -1,86 +1,99 @@
-import argparse
-from moviepy.editor import VideoFileClip
-import whisper
-import os
-import re
-
-def extract_audio(video_path, audio_dir='./audio'):
-    os.makedirs(audio_dir, exist_ok=True)
-    base_filename = os.path.splitext(os.path.basename(video_path))[0]
-    audio_filename = os.path.join(audio_dir, base_filename + '.wav')
-    video_clip = VideoFileClip(video_path)
-    video_clip.audio.write_audiofile(audio_filename)
-    video_clip.close()
-    return audio_filename
-
-def transcribe_audio(audio_path, model_type='base', transcribed_dir='./transcribed'):
-    model = whisper.load_model(model_type)
-    result = model.transcribe(audio_path)
-    
-    os.makedirs(transcribed_dir, exist_ok=True)
-    base_filename = os.path.splitext(os.path.basename(audio_path))[0]
-    transcribed_filename = os.path.join(transcribed_dir, base_filename + '.txt')
-
-    with open(transcribed_filename, 'w') as file:
-        for segment in result['segments']:
-            start = segment['start']
-            end = segment['end']
-            text = segment['text']
-            file.write(f"[{start:.2f}-{end:.2f}] {text}\n")
-    
-    return transcribed_filename, result['text']
-
-def merge_lines(file_path):
-    timestamp_pattern = re.compile(r'\[(\d+\.\d+)-(\d+\.\d+)\]')
-
-    with open(file_path, 'r') as file:
-        lines = file.readlines()
-
-    merged_lines = []
-    i = 0
-
-    while i < len(lines):
-        line = lines[i].strip()
-        match = timestamp_pattern.match(line)
-
-        if match:
-            start_time = float(match.group(1))
-            text = line[match.end():].strip()
-
-            if not (text.endswith('.') or text.endswith('?')):
-                if i + 1 < len(lines):
-                    next_line = lines[i + 1].strip()
-                    next_match = timestamp_pattern.match(next_line)
-                    
-                    if next_match:
-                        end_time = float(next_match.group(2))
-                        next_text = next_line[next_match.end():].strip()
-                        merged_text = text + ' ' + next_text
-                        merged_line = f"[{start_time:.2f}-{end_time:.2f}] {merged_text}\n"
-                        merged_lines.append(merged_line)
-                        i += 1
-            else:
-                end_time = float(match.group(2))
-                merged_lines.append(f"[{start_time:.2f}-{end_time:.2f}] {text}\n")
-
-        i += 1
-
-    with open(file_path, 'w') as file:
-        file.writelines(merged_lines)
-
-    return file_path
-
-def convert_video_to_text(video_file_path, model_type='base'):
-    audio_path = extract_audio(video_file_path)
-    transcribed_path, _ = transcribe_audio(audio_path, model_type)
-    merge_lines(transcribed_path)
-    return transcribed_path
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Transcribe audio from video")
-    parser.add_argument("video_file", help="Path to the video file")
-    parser.add_argument("--model", help="Size of the whisper model (e.g., tiny, base, small, medium, large, huge).", default="base")
-    args = parser.parse_args()
-
-    convert_video_to_text(args.video_file, args.model)
+import argparse
+from moviepy.editor import VideoFileClip
+import whisper
+import os
+import re
+
+# Extracts audio from video
+def extract_audio(video_path, audio_dir='./audio'):
+    os.makedirs(audio_dir, exist_ok=True)
+    base_filename = os.path.splitext(os.path.basename(video_path))[0]
+    audio_filename = os.path.join(audio_dir, base_filename + '.wav')
+    video_clip = VideoFileClip(video_path)
+    video_clip.audio.write_audiofile(audio_filename)
+    video_clip.close()
+    return audio_filename
+
+# Transcribe audio .wav file
+def transcribe_audio(audio_path, model_type='base', transcribed_dir='./transcribed'):
+    model = whisper.load_model(model_type)
+    result = model.transcribe(audio_path)
+    
+    os.makedirs(transcribed_dir, exist_ok=True)
+    base_filename = os.path.splitext(os.path.basename(audio_path))[0]
+    transcribed_filename = os.path.join(transcribed_dir, base_filename + '.txt')
+
+    with open(transcribed_filename, 'w') as file:
+        for segment in result['segments']:
+            start = segment['start']
+            end = segment['end']
+            text = segment['text']
+            file.write(f"[{start:.2f}-{end:.2f}] {text}\n")
+    
+    return transcribed_filename, result['text']
+
+# Merge lines in file that are part of the same sentence
+def merge_lines(file_path):
+    timestamp_pattern = re.compile(r'\[(\d+\.\d+)-(\d+\.\d+)\]')
+
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+
+    merged_lines = []
+    i = 0
+
+    while i < len(lines):
+        line = lines[i].strip()
+        match = timestamp_pattern.match(line)
+
+        if match:
+            start_time = float(match.group(1))
+            text = line[match.end():].strip()
+
+            # Check if line doesnt end
+            if not (text.endswith('.') or text.endswith('?')):
+                # Merge with the next line
+                if i + 1 < len(lines):
+                    next_line = lines[i + 1].strip()
+                    next_match = timestamp_pattern.match(next_line)
+                    
+                    if next_match:
+                        end_time = float(next_match.group(2))
+                        next_text = next_line[next_match.end():].strip()
+                        merged_text = text + ' ' + next_text
+                        merged_line = f"[{start_time:.2f}-{end_time:.2f}] {merged_text}\n"
+                        merged_lines.append(merged_line)
+                        i += 1
+            else:
+                end_time = float(match.group(2))
+                merged_lines.append(f"[{start_time:.2f}-{end_time:.2f}] {text}\n")
+
+        i += 1
+
+    # Overwrite original file with merged lines
+    with open(file_path, 'w') as file:
+        file.writelines(merged_lines)
+
+    return file_path
+
+# Driver function
+def convert_video_to_text(video_file_path, model_type='base'):
+    # Extract audio
+    audio_path = extract_audio(video_file_path)
+
+    # Transcribe audio
+    transcribed_path, _ = transcribe_audio(audio_path, model_type)
+
+    # Merge lines
+    merge_lines(transcribed_path)
+    
+    return transcribed_path
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Transcribe audio from video")
+    parser.add_argument("video_file", help="Path to the video file")
+    parser.add_argument("--model", help="Size of the whisper model (e.g., tiny, base, small, medium, large, huge).", default="base")
+    args = parser.parse_args()
+
+    convert_video_to_text(args.video_file, args.model)