import os import io import tempfile import gradio as gr from dotenv import load_dotenv import torch from scipy.io.wavfile import write from diffusers import DiffusionPipeline from transformers import pipeline from pathlib import Path from PIL import Image import spaces load_dotenv() hf_token = os.getenv("HF_TKN") # Determine if we have access to a GPU device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device_id = 0 if torch.cuda.is_available() else -1 # Initialize the image captioning pipeline captioning_pipeline = pipeline( "image-to-text", model="nlpconnect/vit-gpt2-image-captioning", device=device_id ) # Initialize the text-to-audio pipeline pipe = DiffusionPipeline.from_pretrained( "cvssp/audioldm2", use_auth_token=hf_token ) pipe.to(device) @spaces.GPU(duration=120) def analyze_image_with_free_model(image_file: bytes): """ Analyze the uploaded image using the ViT-GPT2 image captioning pipeline. :param image_file: Binary content of the uploaded image. :return: A tuple (caption, error_flag). caption (str) - The generated caption or error message. error_flag (bool) - Indicates if an error occurred. """ try: # Validate image input if not image_file: return "Error: No image data received.", True # Check if the file is a valid image try: Image.open(io.BytesIO(image_file)).verify() except Exception: return "Error: Invalid image file. Please upload a valid image.", True # Write the valid image to a temporary file for the pipeline with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as temp_file: temp_file.write(image_file) temp_image_path = temp_file.name # Perform image captioning results = captioning_pipeline(temp_image_path) if not results or not isinstance(results, list): return "Error: Captioning pipeline returned invalid results.", True # Extract and clean up the generated caption caption = results[0].get("generated_text", "").strip() if not caption: return "No caption was generated by the model.", True return caption, False except Exception as e: return f"Error analyzing image: {e}", True @spaces.GPU(duration=120) def get_audioldm_from_caption(caption: str): """ Generate an audio file (WAV) from a text caption using the AudioLDM2 pipeline. :param caption: The text prompt used to generate audio. :return: The path to the generated .wav file, or None if an error occurred. """ try: # Move pipeline to GPU (if available) pipe.to(device) # Generate audio from text prompt audio_output = pipe( prompt=caption, num_inference_steps=50, guidance_scale=7.5 ) # Move pipeline back to CPU to free GPU memory pipe.to("cpu") # Extract the first audio sample audio = audio_output.audios[0] # Write the audio to a temporary WAV file with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav: write(temp_wav.name, 16000, audio) return temp_wav.name except Exception as e: print(f"Error generating audio from caption: {e}") return None # Custom CSS for styling the Gradio Blocks css = """ #col-container{ margin: 0 auto; max-width: 800px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML("""

🎶 Generate Sound Effects from Image

âš¡ Powered by Bilsimaging

""") gr.Markdown(""" Welcome to this unique sound effect generator! This tool allows you to upload an image and generate a descriptive caption and a corresponding sound effect, all using free, open-source models on Hugging Face. **💡 How it works:** 1. **Upload an image**: Choose an image that you'd like to analyze. 2. **Generate Description**: Click on 'Generate Description' to get a textual description of your uploaded image. 3. **Generate Sound Effect**: Based on the image description, click on 'Generate Sound Effect' to create a sound effect that matches the image context. Enjoy the journey from visual to auditory sensation with just a few clicks! """) # Define Gradio interface elements image_upload = gr.File(label="Upload Image", type="binary") generate_description_button = gr.Button("Generate Description") caption_display = gr.Textbox(label="Image Description", interactive=False) generate_sound_button = gr.Button("Generate Sound Effect") audio_output = gr.Audio(label="Generated Sound Effect") gr.Markdown(""" ## 👥 How You Can Contribute We welcome contributions and suggestions for improvements. Your feedback is invaluable to the continuous enhancement of this application. For support, questions, or to contribute, please contact us at [contact@bilsimaging.com](mailto:contact@bilsimaging.com). Support our work and get involved by donating through [Ko-fi](https://ko-fi.com/bilsimaging). - Bilel Aroua """) gr.Markdown(""" ## 📢 Stay Connected This app is a testament to the creative possibilities that emerge when technology meets art. Enjoy exploring the auditory landscape of your images! """) # Define the helper functions for Gradio event handlers def update_caption(image_file): description, error_flag = analyze_image_with_free_model(image_file) if error_flag: # In case of error, just return the error message return description return description def generate_sound(description): # Validate the description before generating audio if not description or description.startswith("Error"): return None audio_path = get_audioldm_from_caption(description) return audio_path # Wire the Gradio events to the functions generate_description_button.click( fn=update_caption, inputs=image_upload, outputs=caption_display ) generate_sound_button.click( fn=generate_sound, inputs=caption_display, outputs=audio_output ) gr.HTML( '' '' ) # An extra placeholder if needed html = gr.HTML() # Enable debug and optional share. On Spaces, 'share=True' is typically ignored. demo.launch(debug=True, share=True)