import gradio as gr import whisper import os from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer from docx import Document from reportlab.pdfgen import canvas from reportlab.pdfbase.ttfonts import TTFont from reportlab.pdfbase import pdfmetrics from reportlab.lib.pagesizes import A4 import arabic_reshaper from bidi.algorithm import get_display from pptx import Presentation import subprocess import shlex import yt_dlp # Load the Whisper model (smaller model for faster transcription) model = whisper.load_model("tiny") # Load M2M100 translation model for different languages def load_translation_model(target_language): lang_codes = { "fa": "fa", # Persian (Farsi) "es": "es", # Spanish "fr": "fr", # French "de": "de", # German "it": "it", # Italian "pt": "pt", # Portuguese "ar": "ar", # Arabic "zh": "zh", # Chinese "hi": "hi", # Hindi "ja": "ja", # Japanese "ko": "ko", # Korean "ru": "ru", # Russian } target_lang_code = lang_codes.get(target_language) if not target_lang_code: raise ValueError(f"Translation model for {target_language} not supported") tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") translation_model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") tokenizer.src_lang = "en" tokenizer.tgt_lang = target_lang_code return tokenizer, translation_model def translate_text(text, tokenizer, model): try: inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) translated = model.generate(**inputs, forced_bos_token_id=tokenizer.get_lang_id(tokenizer.tgt_lang)) return tokenizer.decode(translated[0], skip_special_tokens=True) except Exception as e: raise RuntimeError(f"Error during translation: {e}") # Helper function to format timestamps in SRT format def format_timestamp(seconds): milliseconds = int((seconds % 1) * 1000) seconds = int(seconds) hours = seconds // 3600 minutes = (seconds % 3600) // 60 seconds = seconds % 60 return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}" # Corrected write_srt function def write_srt(transcription, output_file, tokenizer=None, translation_model=None): with open(output_file, "w") as f: for i, segment in enumerate(transcription['segments']): start = segment['start'] end = segment['end'] text = segment['text'] if translation_model: text = translate_text(text, tokenizer, translation_model) start_time = format_timestamp(start) end_time = format_timestamp(end) f.write(f"{i + 1}\n") f.write(f"{start_time} --> {end_time}\n") f.write(f"{text.strip()}\n\n") # Embedding subtitles into video (hardsub) def embed_hardsub_in_video(video_file, srt_file, output_video): command = f'ffmpeg -i "{video_file}" -vf "subtitles=\'{srt_file}\'" -c:v libx264 -crf 23 -preset medium "{output_video}"' try: process = subprocess.run(shlex.split(command), capture_output=True, text=True, timeout=300) if process.returncode != 0: raise RuntimeError(f"ffmpeg error: {process.stderr}") except subprocess.TimeoutExpired: raise RuntimeError("ffmpeg process timed out.") except Exception as e: raise RuntimeError(f"Error running ffmpeg: {e}") # Helper function to write Word documents def write_word(transcription, output_file, tokenizer=None, translation_model=None, target_language=None): doc = Document() rtl = target_language == "fa" for i, segment in enumerate(transcription['segments']): text = segment['text'] if translation_model: text = translate_text(text, tokenizer, translation_model) para = doc.add_paragraph(f"{i + 1}. {text.strip()}") if rtl: para.paragraph_format.right_to_left = True doc.save(output_file) # Helper function to reverse text for RTL def reverse_text_for_rtl(text): return ' '.join([word[::-1] for word in text.split()]) # Helper function to write PDF documents def write_pdf(transcription, output_file, tokenizer=None, translation_model=None, target_language=None): # Create PDF with A4 page size c = canvas.Canvas(output_file, pagesize=A4) # Get the directory where app.py is located app_dir = os.path.dirname(os.path.abspath(__file__)) # Define font paths for different languages fonts = { 'fa': os.path.join(app_dir, 'B-NAZANIN.TTF'), # Persian Font 'ar': os.path.join(app_dir, 'Amiri-Regular.ttf'), # Arabic Font 'default': 'Arial' # Default font for other languages } # Register and set the appropriate font font_path = fonts.get(target_language, fonts['default']) if os.path.exists(font_path): try: pdfmetrics.registerFont(TTFont('custom_font', font_path)) c.setFont('custom_font', 12) except Exception as e: raise RuntimeError(f"Error registering font: {e}.") else: raise FileNotFoundError(f"Font file not found at {font_path}. Please ensure it is available.") # Initialize y position from top of page y_position = A4[1] - 50 # Start 50 points from top line_height = 20 # Process each segment for i, segment in enumerate(transcription['segments']): text = segment['text'] # Translate if translation model is provided if translation_model: text = translate_text(text, tokenizer, translation_model) # Format the line with segment number line = f"{i + 1}. {text.strip()}" # For RTL languages like Persian and Arabic, reshape and reorder text if target_language in ['fa', 'ar']: reshaped_text = arabic_reshaper.reshape(line) bidi_text = get_display(reshaped_text) else: bidi_text = line # For LTR languages, no reshaping needed # Add new page if needed if y_position < 50: # Leave 50 points margin at bottom c.showPage() c.setFont('custom_font', 12) y_position = A4[1] - 50 # Draw the text right-aligned for RTL languages, otherwise left-aligned if target_language in ['fa', 'ar']: c.drawRightString(A4[0] - 50, y_position, bidi_text) # Right align for RTL else: c.drawString(50, y_position, bidi_text) # Left align for LTR # Update y position for next line y_position -= line_height # Save the PDF c.save() return output_file # Helper function to write PowerPoint slides def write_ppt(transcription, output_file, tokenizer=None, translation_model=None): ppt = Presentation() for i, segment in enumerate(transcription['segments']): text = segment['text'] if translation_model: text = translate_text(text, tokenizer, translation_model) slide = ppt.slides.add_slide(ppt.slide_layouts[5]) title = slide.shapes.title title.text = f"{i + 1}. {text.strip()}" ppt.save(output_file) # Function to download YouTube video def download_youtube_video(url): ydl_opts = { 'format': 'mp4', 'outtmpl': 'downloaded_video.mp4', } with yt_dlp.YoutubeDL(ydl_opts) as ydl: ydl.download([url]) return 'downloaded_video.mp4' # Transcribing video and generating output def transcribe_video(video_file, video_url, language, target_language, output_format): if video_url: video_file_path = download_youtube_video(video_url) else: video_file_path = video_file.name result = model.transcribe(video_file_path, language=language) video_name = os.path.splitext(video_file_path)[0] if target_language != "en": try: tokenizer, translation_model = load_translation_model(target_language) except Exception as e: raise RuntimeError(f"Error loading translation model: {e}") else: tokenizer, translation_model = None, None srt_file = f"{video_name}.srt" write_srt(result, srt_file, tokenizer, translation_model) if output_format == "SRT": return srt_file elif output_format == "Video with Hardsub": output_video = f"{video_name}_with_subtitles.mp4" try: embed_hardsub_in_video(video_file_path, srt_file, output_video) return output_video except Exception as e: raise RuntimeError(f"Error embedding subtitles in video: {e}") elif output_format == "Word": word_file = f"{video_name}.docx" write_word(result, word_file, tokenizer, translation_model, target_language) return word_file elif output_format == "PDF": pdf_file = f"{video_name}.pdf" write_pdf(result, pdf_file, tokenizer, translation_model) return pdf_file elif output_format == "PowerPoint": ppt_file = f"{video_name}.pptx" write_ppt(result, ppt_file, tokenizer, translation_model) return ppt_file # Gradio interface with YouTube URL iface = gr.Interface( fn=transcribe_video, inputs=[ gr.File(label="Upload Video File (or leave empty for YouTube link)"), # Removed 'optional=True' gr.Textbox(label="YouTube Video URL (optional)", placeholder="https://www.youtube.com/watch?v=..."), gr.Dropdown(label="Select Original Video Language", choices=["en", "es", "fr", "de", "it", "pt"], value="en"), gr.Dropdown(label="Select Subtitle Translation Language", choices=["en", "fa", "es", "de", "fr", "it", "pt"], value="fa"), gr.Radio(label="Choose Output Format", choices=["SRT", "Video with Hardsub", "Word", "PDF", "PowerPoint"], value="Video with Hardsub") ], outputs=gr.File(label="Download File"), title="Video Subtitle Generator with Translation & Multi-Format Output (Supports YouTube)", description=( "This tool allows you to generate subtitles from a video file or YouTube link using Whisper, " "translate the subtitles into multiple languages using M2M100, and export them " "in various formats including SRT, hardcoded subtitles in video, Word, PDF, or PowerPoint." ), theme="compact", live=False ) if __name__ == "__main__": iface.launch()