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| # external imports | |
| import time | |
| import gradio as gr | |
| # local imports | |
| from blip_image_caption_large import Blip_Image_Caption_Large | |
| from phi3_mini_4k_instruct import Phi3_Mini_4k_Instruct | |
| from musicgen_small import Musicgen_Small | |
| #image_to_music function | |
| def image_to_music(image_path): | |
| # test image captioning | |
| image_caption_start_time = time.time() | |
| image_caption_model = Blip_Image_Caption_Large() | |
| test_caption = image_caption_model.caption_image_local_pipeline(image_path) | |
| print(test_caption) | |
| image_caption_end_time = time.time() | |
| # test text generation | |
| text_generation_start_time = time.time() | |
| text_generation_model = Phi3_Mini_4k_Instruct() | |
| #TODO: move this to a config file | |
| text_generation_model.local_pipeline.model.config.max_new_tokens = 200 | |
| #TODO: move system prompt somewhere else, allow for genre override | |
| messages = [ | |
| {"role": "system", "content": "You are an image caption to song description converter with a deep understanding of Music and Art. You are given the caption of an image. Your task is to generate a textual description of a musical piece that fits the caption. The description should be detailed and vivid, and should include the genre, mood, instruments, tempo, and other relevant information about the music. You should also use your knowledge of art and visual aesthetics to create a musical piece that complements the image. Only output the description of the music, without any explanation or introduction. Be concise."}, | |
| {"role": "user", "content": test_caption[0]["generated_text"]}, | |
| ] | |
| test_text = text_generation_model.generate_text_local_pipeline(messages) | |
| print(test_text) | |
| text_generation_end_time = time.time() | |
| # test audio generation | |
| music_generation_start_time = time.time() | |
| music_generation_model = Musicgen_Small() | |
| music_generation_model.generate_music_local_pipeline(str(test_text[-1]['generated_text'][-1]['content'])) | |
| music_generation_end_time = time.time() | |
| # calculate durations | |
| image_caption_duration = image_caption_end_time - image_caption_start_time | |
| text_generation_duration = text_generation_end_time - text_generation_start_time | |
| music_generation_duration = music_generation_end_time - music_generation_start_time | |
| total_duration = music_generation_end_time - image_caption_start_time | |
| # output generated_text, audio and duration to gradio | |
| return (test_caption[0]["generated_text"], test_text[-1]['generated_text'][-1]['content'], "data/musicgen_out.wav", | |
| f"Image Captioning Duration: {image_caption_duration} sec", | |
| f"Text Generation Duration: {text_generation_duration} sec", | |
| f"Music Generation Duration: {music_generation_duration} sec", | |
| f"Total Duration: {total_duration} sec") | |
| # Gradio UI | |
| def gradio(): | |
| # Define Gradio Interface, information from (https://www.gradio.app/docs/chatinterface) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1 style='text-align: center;'> ⛺ Image to Music Generator 🎼</h1>") | |
| image_input = gr.Image(type="filepath", label="Upload Image") | |
| with gr.Row(): | |
| caption_output = gr.Textbox(label="Image Caption") | |
| music_description_output = gr.Textbox(label="Music Description") | |
| durations = gr.Textbox(label="Processing Times", interactive=False, placeholder="Time statistics will appear here") | |
| music_output = gr.Audio(label="Generated Music") | |
| # Button to trigger the process | |
| generate_button = gr.Button("Generate Music") | |
| generate_button.click(fn=image_to_music, inputs=[image_input], outputs=[caption_output, music_description_output, music_output, durations]) | |
| # Launch Gradio app | |
| demo.launch() | |
| gradio() | |