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| import gradio as gr | |
| lpmc_client = gr.load("seungheondoh/LP-Music-Caps-demo", src="spaces") | |
| from gradio_client import Client | |
| client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/") | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") | |
| pipe.to("cuda") | |
| # if using torch < 2.0 | |
| # pipe.enable_xformers_memory_efficient_attention() | |
| from pydub import AudioSegment | |
| def cut_audio(input_path, output_path, max_duration=30000): | |
| audio = AudioSegment.from_file(input_path) | |
| if len(audio) > max_duration: | |
| audio = audio[:max_duration] | |
| audio.export(output_path, format="mp3") | |
| return output_path | |
| def infer(audio_file): | |
| truncated_audio = cut_audio(audio_file, "trunc_audio.mp3") | |
| cap_result = lpmc_client( | |
| truncated_audio, # str (filepath or URL to file) in 'audio_path' Audio component | |
| api_name="predict" | |
| ) | |
| print(cap_result) | |
| summarize_q = f""" | |
| I'll give you a list of music descriptions. Create a summary reflecting the musical ambiance. | |
| Do not processs each segment, but provide a summary for the whole instead. | |
| Here's the list: | |
| {cap_result} | |
| """ | |
| summary_result = client.predict( | |
| summarize_q, # str in 'Message' Textbox component | |
| api_name="/chat_1" | |
| ) | |
| print(f"SUMMARY: {summary_result}") | |
| llama_q = f""" | |
| I'll give you music description, then i want you to provide an illustrative image description that would fit well with the music. | |
| Answer with only one image description. Never do lists. | |
| Here's the music description : | |
| {summary_result} | |
| """ | |
| result = client.predict( | |
| llama_q, # str in 'Message' Textbox component | |
| api_name="/chat_1" | |
| ) | |
| print(result) | |
| images = pipe(prompt=result).images | |
| #return cap_result, result, images | |
| return images | |
| css = """ | |
| #col-container {max-width: 510px; margin-left: auto; margin-right: auto;} | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;"> | |
| Music To Image | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Sends an audio into <a href="https://huggingface.co/spaces/seungheondoh/LP-Music-Caps-demo" target="_blank">LP-Music-Caps</a> | |
| to generate a audio caption which is then translated to an illustrative image description with Llama2, and finally run through | |
| Stable Diffusion XL to generate an image from the audio ! <br /><br /> | |
| Note: Only the first 30 seconds of your audio will be used for inference. | |
| </p> | |
| </div>""") | |
| audio_input = gr.Audio(type="filepath", source="upload") | |
| infer_btn = gr.Button("Generate Image from Music") | |
| #lpmc_cap = gr.Textbox(label="Lp Music Caps caption") | |
| #llama_trans_cap = gr.Textbox(label="Llama translation") | |
| img_result = gr.gallerG(label="Result", grid=2) | |
| #infer_btn.click(fn=infer, inputs=[audio_input], outputs=[lpmc_cap, llama_trans_cap, img_result]) | |
| infer_btn.click(fn=infer, inputs=[audio_input], outputs=[img_result]) | |
| demo.queue().launch() |