Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import fitz # PyMuPDF | |
| import edge_tts | |
| import asyncio | |
| import os | |
| import uuid | |
| from pydub import AudioSegment | |
| # Fetch available voices | |
| async def get_voices(): | |
| voices = await edge_tts.VoicesManager.create() | |
| voice_list = voices.find(Language="en") | |
| return {f"{v['FriendlyName']} ({v['ShortName']})": v['ShortName'] for v in voice_list} | |
| # Function to extract text from PDF | |
| def extract_text(pdf_file): | |
| doc = fitz.open(pdf_file.name) | |
| return "".join([page.get_text() for page in doc]) | |
| # Main conversion logic with Progress Tracker | |
| async def convert_pdf_to_long_audio(pdf_file, voice_short_name, progress=gr.Progress()): | |
| if pdf_file is None: | |
| return "Please upload a file.", None | |
| progress(0, desc="Reading PDF...") | |
| text = extract_text(pdf_file) | |
| if not text.strip(): | |
| return "No text found in PDF.", None | |
| # Chunking: ~2500 characters is a safe bet for Edge-TTS stability | |
| chunk_size = 2500 | |
| chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)] | |
| total_chunks = len(chunks) | |
| combined_audio = AudioSegment.empty() | |
| session_id = str(uuid.uuid4()) | |
| os.makedirs(session_id, exist_ok=True) | |
| # Iterating through chunks with progress updates | |
| for i, chunk in enumerate(chunks): | |
| # Update progress bar: (current_index / total_count) | |
| progress((i / total_chunks), desc=f"Converting chunk {i+1} of {total_chunks} to voice...") | |
| chunk_path = os.path.join(session_id, f"chunk_{i}.mp3") | |
| communicate = edge_tts.Communicate(chunk, voice_short_name) | |
| await communicate.save(chunk_path) | |
| # Load and append | |
| segment = AudioSegment.from_mp3(chunk_path) | |
| combined_audio += segment | |
| progress(0.95, desc="Merging all audio parts into final file...") | |
| final_path = f"audiobook_{session_id}.mp3" | |
| combined_audio.export(final_path, format="mp3") | |
| # Cleanup | |
| for f in os.listdir(session_id): | |
| os.remove(os.path.join(session_id, f)) | |
| os.rmdir(session_id) | |
| progress(1.0, desc="Done!") | |
| return text[:2000] + "...", final_path | |
| # Gradio Wrapper | |
| def process(pdf, voice_name, progress=gr.Progress()): | |
| voice_id = voice_dict[voice_name] | |
| return asyncio.run(convert_pdf_to_long_audio(pdf, voice_id, progress)) | |
| # Initialize voice list | |
| voice_dict = asyncio.run(get_voices()) | |
| # Building the Interface | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# 🎧 Infinite PDF Audiobook Generator") | |
| gr.Markdown("Upload your PDF and wait for the AI to narrate it. **Progress is tracked below the button.**") | |
| with gr.Row(): | |
| with gr.Column(): | |
| pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"]) | |
| voice_input = gr.Dropdown( | |
| choices=list(voice_dict.keys()), | |
| label="Select AI Voice", | |
| value="Microsoft Guy Online (Natural) - en-US-GuyNeural" | |
| ) | |
| btn = gr.Button("Start Audio Conversion", variant="primary") | |
| with gr.Column(): | |
| text_preview = gr.Textbox(label="Text Preview", lines=5) | |
| audio_output = gr.Audio(label="Final Audiobook (Download here)") | |
| # The magic happens here: passing the progress bar to the function | |
| btn.click(process, inputs=[pdf_input, voice_input], outputs=[text_preview, audio_output]) | |
| if __name__ == "__main__": | |
| demo.launch() |