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)