import gradio as gr import whisper import os from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer from docx import Document from fpdf import FPDF 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): pdf = FPDF() pdf.add_page() font_path = "/home/user/app/B-NAZANIN.TTF" pdf.add_font('B-NAZANIN', '', font_path, uni=True) pdf.set_font('B-NAZANIN', size=12) for i, segment in enumerate(transcription['segments']): text = segment['text'] if translation_model: text = translate_text(text, tokenizer, translation_model) reversed_text = reverse_text_for_rtl(text) pdf.multi_cell(0, 10, f"{i + 1}. {reversed_text.strip()}", align='L') pdf.output(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()