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app.py

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+import gradio as gr
+import whisper
+import os
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+from docx import Document  # For Word output
+from fpdf import FPDF  # For PDF output
+from pptx import Presentation  # For PowerPoint output
+import subprocess  # To use ffmpeg for embedding subtitles
+import shlex  # For better command-line argument handling
+
+# Load the Whisper model
+model = whisper.load_model("tiny")  # Smaller model for faster transcription
+
+# 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
+    }
+    target_lang_code = lang_codes.get(target_language)
+    if not target_lang_code:
+        raise ValueError(f"Translation model for {target_language} not supported")
+
+    # Load M2M100 model and tokenizer
+    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 (hh:mm:ss,ms)
+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")
+
+def embed_hardsub_in_video(video_file, srt_file, output_video):
+    """Uses ffmpeg to burn subtitles into the video (hardsub)."""
+    command = f'ffmpeg -i "{video_file}" -vf "subtitles=\'{srt_file}\'" -c:v libx264 -crf 23 -preset medium "{output_video}"'
+    
+    try:
+        print(f"Running command: {command}")  # Debug statement
+        process = subprocess.run(shlex.split(command), capture_output=True, text=True, timeout=300)
+        print(f"ffmpeg output: {process.stdout}")  # Debug statement
+        if process.returncode != 0:
+            raise RuntimeError(f"ffmpeg error: {process.stderr}")  # Print the error
+    except subprocess.TimeoutExpired:
+        raise RuntimeError("ffmpeg process timed out.")
+    except Exception as e:
+        raise RuntimeError(f"Error running ffmpeg: {e}")
+
+def write_word(transcription, output_file, tokenizer=None, translation_model=None):
+    """Creates a Word document from the transcription."""
+    doc = Document()
+    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)
+        
+        doc.add_paragraph(f"{i + 1}. [{format_timestamp(start)} - {format_timestamp(end)}] {text.strip()}")
+    doc.save(output_file)
+
+def write_pdf(transcription, output_file, tokenizer=None, translation_model=None):
+    """Creates a PDF document from the transcription."""
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+    
+    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)
+        
+        pdf.multi_cell(0, 10, f"{i + 1}. [{format_timestamp(start)} - {format_timestamp(end)}] {text.strip()}")
+    
+    pdf.output(output_file)
+
+def write_ppt(transcription, output_file, tokenizer=None, translation_model=None):
+    """Creates a PowerPoint presentation from the transcription."""
+    ppt = Presentation()
+    
+    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)
+        
+        slide = ppt.slides.add_slide(ppt.slide_layouts[5])  # Blank slide
+        title = slide.shapes.title
+        title.text = f"{i + 1}. [{format_timestamp(start)} - {format_timestamp(end)}] {text.strip()}"
+    
+    ppt.save(output_file)
+
+def transcribe_video(video_file, language, target_language, output_format):
+    # Transcribe the video with Whisper
+    result = model.transcribe(video_file.name, language=language)
+    video_name = os.path.splitext(video_file.name)[0]
+    
+    # Load the translation model for the selected subtitle language
+    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
+
+    # Save the SRT file
+    srt_file = f"{video_name}.srt"
+    write_srt(result, srt_file, tokenizer, translation_model)
+
+    # Output based on user's selection
+    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.name, 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)
+        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
+iface = gr.Interface(
+    fn=transcribe_video,
+    inputs=[
+        gr.File(label="Upload Video"),
+        gr.Dropdown(label="Select Video Language", choices=["en", "es", "fr", "de", "it", "pt"], value="en"),
+        gr.Dropdown(label="Select Subtitle Language", choices=["en", "fa", "es", "fr"], value="fa"),
+        gr.Radio(label="Output Format", choices=["SRT", "Video with Hardsub", "Word", "PDF", "PowerPoint"], value="Video with Hardsub")
+    ],
+    outputs=gr.File(label="Download Subtitles, Video, or Document"),
+    title="Video Subtitle Generator with Hardsub and Document Formats",
+    description="Upload a video file to generate subtitles in SRT format, download the video with hardsubbed subtitles, or generate Word, PDF, or PowerPoint documents using Whisper and M2M100 for translation."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+transformers>=4.30.0
+gradio>=3.16.0
+ffmpeg-python
+python-docx
+fpdf
+python-pptx
+sentencepiece  # Required for M2M100 and MarianMT translation models
+librosa  # Required for audio processing
+git+https://github.com/openai/whisper.git